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- Title
- Design of a Test Framework for the Evaluation of Transfer Learning Algorithms.
- Creator
- Weiss, Karl Robert, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
A traditional machine learning environment is characterized by the training and testing data being drawn from the same domain, therefore, having similar distribution characteristics. In contrast, a transfer learning environment is characterized by the training data having di erent distribution characteristics from the testing data. Previous research on transfer learning has focused on the development and evaluation of transfer learning algorithms using real-world datasets. Testing with real...
Show moreA traditional machine learning environment is characterized by the training and testing data being drawn from the same domain, therefore, having similar distribution characteristics. In contrast, a transfer learning environment is characterized by the training data having di erent distribution characteristics from the testing data. Previous research on transfer learning has focused on the development and evaluation of transfer learning algorithms using real-world datasets. Testing with real-world datasets exposes an algorithm to a limited number of data distribution di erences and does not exercise an algorithm's full capability and boundary limitations. In this research, we de ne, implement, and deploy a transfer learning test framework to test machine learning algorithms. The transfer learning test framework is designed to create a wide-range of distribution di erences that are typically encountered in a transfer learning environment. By testing with many di erent distribution di erences, an algorithm's strong and weak points can be discovered and evaluated against other algorithms. This research additionally performs case studies that use the transfer learning test framework. The rst case study focuses on measuring the impact of exposing algorithms to the Domain Class Imbalance distortion pro le. The next case study uses the entire transfer learning test framework to evaluate both transfer learning and traditional machine learning algorithms. The nal case study uses the transfer learning test framework in conjunction with real-world datasets to measure the impact of the base traditional learner on the performance of transfer learning algorithms. Two additional experiments are performed that are focused on using unique realworld datasets. The rst experiment uses transfer learning techniques to predict fraudulent Medicare claims. The second experiment uses a heterogeneous transfer learning method to predict phishing webgages. These case studies will be of interest to researchers who develop and improve transfer learning algorithms. This research will also be of bene t to machine learning practitioners in the selection of high-performing transfer learning algorithms.
Show less - Date Issued
- 2017
- PURL
- http://purl.flvc.org/fau/fd/FA00005925
- Subject Headings
- Dissertations, Academic -- Florida Atlantic University, Machine learning., Algorithms., Machine learning Development.
- Format
- Document (PDF)
- Title
- PRESERVING KNOWLEDGE IN SIMULATED BEHAVIORAL ACTION LOOPS.
- Creator
- St.Clair, Rachel, Barenholtz, Elan, Hahn, William, Florida Atlantic University, Center for Complex Systems and Brain Sciences, Charles E. Schmidt College of Science
- Abstract/Description
-
One basic goal of artificial learning systems is the ability to continually learn throughout that system’s lifetime. Transitioning between tasks and re-deploying prior knowledge is thus a desired feature of artificial learning. However, in the deep-learning approaches, the problem of catastrophic forgetting of prior knowledge persists. As a field, we want to solve the catastrophic forgetting problem without requiring exponential computations or time, while demonstrating real-world relevance....
Show moreOne basic goal of artificial learning systems is the ability to continually learn throughout that system’s lifetime. Transitioning between tasks and re-deploying prior knowledge is thus a desired feature of artificial learning. However, in the deep-learning approaches, the problem of catastrophic forgetting of prior knowledge persists. As a field, we want to solve the catastrophic forgetting problem without requiring exponential computations or time, while demonstrating real-world relevance. This work proposes a novel model which uses an evolutionary algorithm similar to a meta-learning objective, that is fitted with a resource constraint metrics. Four reinforcement learning environments are considered with the shared concept of depth although the collection of environments is multi-modal. This system shows preservation of some knowledge in sequential task learning and protection of catastrophic forgetting in deep neural networks.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00013896
- Subject Headings
- Artificial intelligence, Deep learning (Machine learning), Reinforcement learning, Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- Limits on computational precision of image compression transformations.
- Creator
- Schmalz, Mark S., Ritter, G. X., Caimi, F. M., Harbor Branch Oceanographic Institute
- Date Issued
- 1998
- PURL
- http://purl.flvc.org/FCLA/DT/3180417
- Subject Headings
- Image compression, Adaptive computing, Adaptive computation and machine learning
- Format
- Document (PDF)
- Title
- Analysis of machine learning algorithms on bioinformatics data of varying quality.
- Creator
- Shanab, Ahmad Abu, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
One of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One...
Show moreOne of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One particular challenge encountered when analyzing bioinformatics datasets is data noise, which refers to incorrect or missing values in datasets. Noise can be introduced as a result of experimental errors (e.g. faulty microarray chips, insufficient resolution, image corruption, and incorrect laboratory procedures), as well as other errors (errors during data processing, transfer, and/or mining). A special type of data noise called class noise, which occurs when an instance/example is mislabeled. Previous research showed that class noise has a detrimental impact on machine learning algorithms (e.g. worsened classification performance and unstable feature selection). In addition to data noise, gene expression datasets can suffer from the problems of high dimensionality (a very large feature space) and class imbalance (unequal distribution of instances between classes). As a result of these inherent problems, constructing accurate classification models becomes more challenging.
Show less - Date Issued
- 2015
- PURL
- http://purl.flvc.org./fau/fd/FA00004425, http://purl.flvc.org/fau/fd/FA00004425
- Subject Headings
- Artificial intelligence, Bioinformatics, Machine learning, System design, Theory of computation
- Format
- Document (PDF)
- Title
- Big Data Analytics and Engineering for Medicare Fraud Detection.
- Creator
- Herland, Matthew Andrew, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The United States (U.S.) healthcare system produces an enormous volume of data with a vast number of financial transactions generated by physicians administering healthcare services. This makes healthcare fraud difficult to detect, especially when there are considerably less fraudulent transactions than non-fraudulent. Fraud is an extremely important issue for healthcare, as fraudulent activities within the U.S. healthcare system contribute to significant financial losses. In the U.S., the...
Show moreThe United States (U.S.) healthcare system produces an enormous volume of data with a vast number of financial transactions generated by physicians administering healthcare services. This makes healthcare fraud difficult to detect, especially when there are considerably less fraudulent transactions than non-fraudulent. Fraud is an extremely important issue for healthcare, as fraudulent activities within the U.S. healthcare system contribute to significant financial losses. In the U.S., the elderly population continues to rise, increasing the need for programs, such as Medicare, to help with associated medical expenses. Unfortunately, due to healthcare fraud, these programs are being adversely affected, draining resources and reducing the quality and accessibility of necessary healthcare services. In response, advanced data analytics have recently been explored to detect possible fraudulent activities. The Centers for Medicare and Medicaid Services (CMS) released several ‘Big Data’ Medicare claims datasets for different parts of their Medicare program to help facilitate this effort. In this dissertation, we employ three CMS Medicare Big Data datasets to evaluate the fraud detection performance available using advanced data analytics techniques, specifically machine learning. We use two distinct approaches, designated as anomaly detection and traditional fraud detection, where each have very distinct data processing and feature engineering. Anomaly detection experiments classify by provider specialty, determining whether outlier physicians within the same specialty signal fraudulent behavior. Traditional fraud detection refers to the experiments directly classifying physicians as fraudulent or non-fraudulent, leveraging machine learning algorithms to discriminate between classes. We present our novel data engineering approaches for both anomaly detection and traditional fraud detection including data processing, fraud mapping, and the creation of a combined dataset consisting of all three Medicare parts. We incorporate the List of Excluded Individuals and Entities database to identify real world fraudulent physicians for model evaluation. Regarding features, the final datasets for anomaly detection contain only claim counts for every procedure a physician submits while traditional fraud detection incorporates aggregated counts and payment information, specialty, and gender. Additionally, we compare cross-validation to the real world application of building a model on a training dataset and evaluating on a separate test dataset for severe class imbalance and rarity.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013215
- Subject Headings
- Big data, Medicare fraud, Data analytics, Machine learning
- Format
- Document (PDF)
- Title
- DEEP MAXOUT NETWORKS FOR CLASSIFICATION PROBLEMS ACROSS MULTIPLE DOMAINS.
- Creator
- Castaneda, Gabriel, Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
Machine learning techniques such as deep neural networks have become an indispensable tool for a wide range of applications such as image classification, speech recognition, and sentiment analysis in text. An activation function is a mathematical equation that determines the output of each neuron in the neural network. In deep learning architectures the choice of activation functions is very important to the network’s performance. Activation functions determine the output of the model, its...
Show moreMachine learning techniques such as deep neural networks have become an indispensable tool for a wide range of applications such as image classification, speech recognition, and sentiment analysis in text. An activation function is a mathematical equation that determines the output of each neuron in the neural network. In deep learning architectures the choice of activation functions is very important to the network’s performance. Activation functions determine the output of the model, its computational efficiency, and its ability to train and converge after multiple iterations of training epochs. The selection of an activation function is critical to building and training an effective and efficient neural network. In real-world applications of deep neural networks, the activation function is a hyperparameter. We have observed a lack of consensus on how to select a good activation function for a deep neural network, and that a specific function may not be suitable for all domain-specific applications.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013362
- Subject Headings
- Classification, Machine learning--Technique, Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- Enhancement of Deep Neural Networks and Their Application to Text Mining.
- Creator
- Prusa, Joseph Daniel, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Many current application domains of machine learning and arti cial intelligence involve knowledge discovery from text, such as sentiment analysis, document ontology, and spam detection. Humans have years of experience and training with language, enabling them to understand complicated, nuanced text passages with relative ease. A text classi er attempts to emulate or replicate this knowledge so that computers can discriminate between concepts encountered in text; however, learning high-level...
Show moreMany current application domains of machine learning and arti cial intelligence involve knowledge discovery from text, such as sentiment analysis, document ontology, and spam detection. Humans have years of experience and training with language, enabling them to understand complicated, nuanced text passages with relative ease. A text classi er attempts to emulate or replicate this knowledge so that computers can discriminate between concepts encountered in text; however, learning high-level concepts from text, such as those found in many applications of text classi- cation, is a challenging task due to the many challenges associated with text mining and classi cation. Recently, classi ers trained using arti cial neural networks have been shown to be e ective for a variety of text mining tasks. Convolutional neural networks have been trained to classify text from character-level input, automatically learn high-level abstract representations and avoiding the need for human engineered features. This dissertation proposes two new techniques for character-level learning, log(m) character embedding and convolutional window classi cation. Log(m) embedding is a new character-vector representation for text data that is more compact and memory e cient than previous embedding vectors. Convolutional window classi cation is a technique for classifying long documents, i.e. documents with lengths exceeding the input dimension of the neural network. Additionally, we investigate the performance of convolutional neural networks combined with long short-term memory networks, explore how document length impacts classi cation performance and compare performance of neural networks against non-neural network-based learners in text classi cation tasks.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00005959
- Subject Headings
- Text Mining, Neural networks (Computer science), Machine learning
- Format
- Document (PDF)
- Title
- INVESTIGATING MACHINE LEARNING ALGORITHMS WITH IMBALANCED BIG DATA.
- Creator
- Hasanin, Tawfiq, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Recent technological developments have engendered an expeditious production of big data and also enabled machine learning algorithms to produce high-performance models from such data. Nonetheless, class imbalance (in binary classifications) between the majority and minority classes in big data can skew the predictive performance of the classification algorithms toward the majority (negative) class whereas the minority (positive) class usually holds greater value for the decision makers. Such...
Show moreRecent technological developments have engendered an expeditious production of big data and also enabled machine learning algorithms to produce high-performance models from such data. Nonetheless, class imbalance (in binary classifications) between the majority and minority classes in big data can skew the predictive performance of the classification algorithms toward the majority (negative) class whereas the minority (positive) class usually holds greater value for the decision makers. Such bias may lead to adverse consequences, some of them even life-threatening, when the existence of false negatives is generally costlier than false positives. The size of the minority class can vary from fair to extraordinary small, which can lead to different performance scores for machine learning algorithms. Class imbalance is a well-studied area for traditional data, i.e., not big data. However, there is limited research focusing on both rarity and severe class imbalance in big data.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013316
- Subject Headings
- Algorithms, Machine learning, Big data--Data processing, Big data
- Format
- Document (PDF)
- Title
- STATISTICAL MODELING OF SHIP AIRWAKES INCLUDING THE FEASIBILITY OF APPLYING MACHINE LEARNING.
- Creator
- Krishnan, Vaishakh, Gaonkar, Gopal, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
- Abstract/Description
-
Airwakes are shed behind the ship’s superstructure and represent a highly turbulent and rapidly distorting flow field. This flow field severely affects pilot’s workload and such helicopter shipboard operations. It requires both the one-point statistics of autospectrum and the two-point statistics of coherence (normalized cross-spectrum) for a relatively complete description. Recent advances primarily refer to generating databases of flow velocity points through experimental and computational...
Show moreAirwakes are shed behind the ship’s superstructure and represent a highly turbulent and rapidly distorting flow field. This flow field severely affects pilot’s workload and such helicopter shipboard operations. It requires both the one-point statistics of autospectrum and the two-point statistics of coherence (normalized cross-spectrum) for a relatively complete description. Recent advances primarily refer to generating databases of flow velocity points through experimental and computational fluid dynamics (CFD) investigations, numerically computing autospectra along with a few cases of cross-spectra and coherences, and developing a framework for extracting interpretive models of autospectra in closed form from a database along with an application of this framework to study the downwash effects. By comparison, relatively little is known about coherences. In fact, even the basic expressions of cross-spectra and coherences for three components of homogeneous isotropic turbulence (HIT) vary from one study to the other, and the related literature is scattered and piecemeal. Accordingly, this dissertation begins with a unified account of all the cross-spectra and coherences of HIT from first principles. Then, it presents a framework for constructing interpretive coherence models of airwake from a database on the basis of perturbation theory. For each velocity component, the coherence is represented by a separate perturbation series in which the basis function or the first term on the right-hand side of the series is represented by the corresponding coherence for HIT. The perturbation series coefficients are evaluated by satisfying the theoretical constraints and fitting a curve in a least squares sense on a set of numerically generated coherence points from a database. Although not tested against a specific database, the framework has a mathematical basis. Moreover, for assumed values of perturbation series constants, coherence results are presented to demonstrate how coherences of airwakes and such flow fields compare to those of HIT.
Show less - Date Issued
- 2020
- PURL
- http://purl.flvc.org/fau/fd/FA00013629
- Subject Headings
- Ships--Aerodynamics, Turbulence--Statistical methods, Machine learning
- Format
- Document (PDF)
- Title
- PREDICTING MELANOMA RISK FROM ELECTRONIC HEALTH RECORDS WITH MACHINE LEARNING TECHNIQUES.
- Creator
- Richter, Aaron N., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Melanoma is one of the fastest growing cancers in the world, and can affect patients earlier in life than most other cancers. Therefore, it is imperative to be able to identify patients at high risk for melanoma and enroll them in screening programs to detect the cancer early. Electronic health records collect an enormous amount of data about real-world patient encounters, treatments, and outcomes. This data can be mined to increase our understanding of melanoma as well as build personalized...
Show moreMelanoma is one of the fastest growing cancers in the world, and can affect patients earlier in life than most other cancers. Therefore, it is imperative to be able to identify patients at high risk for melanoma and enroll them in screening programs to detect the cancer early. Electronic health records collect an enormous amount of data about real-world patient encounters, treatments, and outcomes. This data can be mined to increase our understanding of melanoma as well as build personalized models to predict risk of developing the cancer. Cancer risk models built from structured clinical data are limited in current research, with most studies involving just a few variables from institutional databases or registries. This dissertation presents data processing and machine learning approaches to build melanoma risk models from a large database of de-identified electronic health records. The database contains consistently captured structured data, enabling the extraction of hundreds of thousands of data points each from millions of patient records. Several experiments are performed to build effective models, particularly to predict sentinel lymph node metastasis in known melanoma patients and to predict individual risk of developing melanoma. Data for these models suffer from high dimensionality and class imbalance. Thus, classifiers such as logistic regression, support vector machines, random forest, and XGBoost are combined with advanced modeling techniques such as feature selection and data sampling. Risk factors are evaluated using regression model weights and decision trees, while personalized predictions are provided through random forest decomposition and Shapley additive explanations. Random undersampling on the melanoma risk dataset shows that many majority samples can be removed without a decrease in model performance. To determine how much data is truly needed, we explore learning curve approximation methods on the melanoma data and three publicly-available large-scale biomedical datasets. We apply an inverse power law model as well as introduce a novel semi-supervised curve creation method that utilizes a small amount of labeled data.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013342
- Subject Headings
- Melanoma, Electronic Health Records, Machine learning--Technique, Big Data
- Format
- Document (PDF)
- Title
- MACHINE LEARNING APPROACH FOR VEGETATION CLASSIFICATION USING UAS MULTISPECTRAL IMAGERY.
- Creator
- Kesavan, Pandiyan, Sudhagar Nagarajan, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
- Abstract/Description
-
Vegetation monitoring plays a significant role in improving the quality of life above the earth's surface. However, vegetation resources management is challenging due to climate change, global warming, and urban development. The research aims to identify and extract vegetation communities for Jupiter Inlet Lighthouse Outstanding Natural Area (JILONA) using developed Unmanned Aerial Systems (UAS) deployed with five bands of RedEdge Micasence Multispectral Sensor. UAS has a lot of potential for...
Show moreVegetation monitoring plays a significant role in improving the quality of life above the earth's surface. However, vegetation resources management is challenging due to climate change, global warming, and urban development. The research aims to identify and extract vegetation communities for Jupiter Inlet Lighthouse Outstanding Natural Area (JILONA) using developed Unmanned Aerial Systems (UAS) deployed with five bands of RedEdge Micasence Multispectral Sensor. UAS has a lot of potential for various applications as it provides high-resolution imagery at lower altitudes. In this study, spectral reflectance values for each vegetation species were collected using a spectroradiometer instrument. Those values were correlated with five band UAS Image values to understand the sensor's performance, also added with reflectance’s similarities and divergence for vegetation species. Pixel and Object-based classification methods were performed using 0.15 ft Multispectral Imagery to identify the vegetation classes. Supervised Machine Learning Support Vector Machine (SVM) and Random Forest (RF) algorithms with topographical information were used to produce thematic vegetation maps. The Pixel-based procedure using the SVM algorithm generated an overall accuracy and kappa coefficient of above 90 percent. Both classification approaches have provided aesthetic vegetation thematic maps. According to statistical cross-validation findings and visual interpretation of vegetation communities, the pixel classification method outperformed object-based classification.
Show less - Date Issued
- 2021
- PURL
- http://purl.flvc.org/fau/fd/FA00013768
- Subject Headings
- Vegetation classification, Machine learning, Multispectral imaging, Unmanned aerial vehicles
- Format
- Document (PDF)
- Title
- Machine learning techniques for alleviating inherent difficulties in bioinformatics data.
- Creator
- Dittman, David, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
In response to the massive amounts of data that make up a large number of bioinformatics datasets, it has become increasingly necessary for researchers to use computers to aid them in their endeavors. With difficulties such as high dimensionality, class imbalance, noisy data, and difficult to learn class boundaries, being present within the data, bioinformatics datasets are a challenge to work with. One potential source of assistance is the domain of data mining and machine learning, a field...
Show moreIn response to the massive amounts of data that make up a large number of bioinformatics datasets, it has become increasingly necessary for researchers to use computers to aid them in their endeavors. With difficulties such as high dimensionality, class imbalance, noisy data, and difficult to learn class boundaries, being present within the data, bioinformatics datasets are a challenge to work with. One potential source of assistance is the domain of data mining and machine learning, a field which focuses on working with these large amounts of data and develops techniques to discover new trends and patterns that are hidden within the data and to increases the capability of researchers and practitioners to work with this data. Within this domain there are techniques designed to eliminate irrelevant or redundant features, balance the membership of the classes, handle errors found in the data, and build predictive models for future data.
Show less - Date Issued
- 2015
- PURL
- http://purl.flvc.org/fau/fd/FA00004362, http://purl.flvc.org/fau/fd/FA00004362
- Subject Headings
- Artificial intelligence, Bioinformatics, Machine learning, System design, Theory of computation
- Format
- Document (PDF)
- Title
- Machine Learning Algorithms for the Analysis of Social Media and Detection of Malicious User Generated Content.
- Creator
- Heredia, Brian, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
One of the de ning characteristics of the modern Internet is its massive connectedness, with information and human connection simply a few clicks away. Social media and online retailers have revolutionized how we communicate and purchase goods or services. User generated content on the web, through social media, plays a large role in modern society; Twitter has been in the forefront of political discourse, with politicians choosing it as their platform for disseminating information, while...
Show moreOne of the de ning characteristics of the modern Internet is its massive connectedness, with information and human connection simply a few clicks away. Social media and online retailers have revolutionized how we communicate and purchase goods or services. User generated content on the web, through social media, plays a large role in modern society; Twitter has been in the forefront of political discourse, with politicians choosing it as their platform for disseminating information, while websites like Amazon and Yelp allow users to share their opinions on products via online reviews. The information available through these platforms can provide insight into a host of relevant topics through the process of machine learning. Speci - cally, this process involves text mining for sentiment analysis, which is an application domain of machine learning involving the extraction of emotion from text. Unfortunately, there are still those with malicious intent and with the changes to how we communicate and conduct business, comes changes to their malicious practices. Social bots and fake reviews plague the web, providing incorrect information and swaying the opinion of unaware readers. The detection of these false users or posts from reading the text is di cult, if not impossible, for humans. Fortunately, text mining provides us with methods for the detection of harmful user generated content. This dissertation expands the current research in sentiment analysis, fake online review detection and election prediction. We examine cross-domain sentiment analysis using tweets and reviews. Novel techniques combining ensemble and feature selection methods are proposed for the domain of online spam review detection. We investigate the ability for the Twitter platform to predict the United States 2016 presidential election. In addition, we determine how social bots in uence this prediction.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013067
- Subject Headings
- Machine learning., Text mining., User-generated content., Social media.
- Format
- Document (PDF)
- Title
- Machine Learning Methods to Understand Textual Data.
- Creator
- Sohangir, Sahar, Wang, Dingding, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The amount of textual data that produce every minute on the internet is extremely high. Processing of this tremendous volume of mostly unstructured data is not a straightforward function. But the enormous amount of useful information that lay down on them motivate scientists to investigate efficient and effective techniques and algorithms to discover meaningful patterns. Social network applications provide opportunities for people around the world to be in contact and share their valuable...
Show moreThe amount of textual data that produce every minute on the internet is extremely high. Processing of this tremendous volume of mostly unstructured data is not a straightforward function. But the enormous amount of useful information that lay down on them motivate scientists to investigate efficient and effective techniques and algorithms to discover meaningful patterns. Social network applications provide opportunities for people around the world to be in contact and share their valuable knowledge, such as chat, comments, and discussion boards. People usually do not care about spelling and accurate grammatical construction of a sentence in everyday life conversations. Therefore, extracting information from such datasets are more complicated. Text mining can be a solution to this problem. Text mining is a knowledge discovery process used to extract patterns from natural language. Application of text mining techniques on social networking websites can reveal a significant amount of information. Text mining in conjunction with social networks can be used for finding a general opinion about any special subject, human thinking patterns, and group identification. In this study, we investigate machine learning methods in textual data in six chapters.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013107
- Subject Headings
- Machine learning, Internet--Data processing, Text Mining
- Format
- Document (PDF)
- Title
- Alleviating class imbalance using data sampling: Examining the effects on classification algorithms.
- Creator
- Napolitano, Amri E., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Imbalanced class distributions typically cause poor classifier performance on the minority class, which also tends to be the class with the highest cost of mis-classification. Data sampling is a common solution to this problem, and numerous sampling techniques have been proposed to address it. Prior research examining the performance of these techniques has been narrow and limited. This work uses thorough empirical experimentation to compare the performance of seven existing data sampling...
Show moreImbalanced class distributions typically cause poor classifier performance on the minority class, which also tends to be the class with the highest cost of mis-classification. Data sampling is a common solution to this problem, and numerous sampling techniques have been proposed to address it. Prior research examining the performance of these techniques has been narrow and limited. This work uses thorough empirical experimentation to compare the performance of seven existing data sampling techniques using five different classifiers and four different datasets. The work addresses which sampling techniques produce the best performance in the presence of class unbalance, which classifiers are most robust to the problem, as well as which sampling techniques perform better or worse with each classifier. Extensive statistical analysis of these results is provided, in addition to an examination of the qualitative effects of the sampling techniques on the types of predictions made by the C4.5 classifier.
Show less - Date Issued
- 2006
- PURL
- http://purl.flvc.org/fcla/dt/13413
- Subject Headings
- Combinatorial group theory, Data mining, Decision trees, Machine learning
- Format
- Document (PDF)
- Title
- Learning in connectionist networks using the Alopex algorithm.
- Creator
- Venugopal, Kootala Pattath., Florida Atlantic University, Pandya, Abhijit S., Sudhakar, Raghavan, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The Alopex algorithm is presented as a universal learning algorithm for connectionist models. It is shown that the Alopex procedure could be used efficiently as a supervised learning algorithm for such models. The algorithm is demonstrated successfully on a variety of network architectures. Such architectures include multilayer perceptrons, time-delay models, asymmetric, fully recurrent networks and memory neuron networks. The learning performance as well as the generation capability of the...
Show moreThe Alopex algorithm is presented as a universal learning algorithm for connectionist models. It is shown that the Alopex procedure could be used efficiently as a supervised learning algorithm for such models. The algorithm is demonstrated successfully on a variety of network architectures. Such architectures include multilayer perceptrons, time-delay models, asymmetric, fully recurrent networks and memory neuron networks. The learning performance as well as the generation capability of the Alopex algorithm are compared with those of the backpropagation procedure, concerning a number of benchmark problems, and it is shown that the Alopex has specific advantages over the backpropagation. Two new architectures (gain layer schemes) are proposed for the on-line, direct adaptive control of dynamical systems using neural networks. The proposed schemes are shown to provide better dynamic response and tracking characteristics, than the other existing direct control schemes. A velocity reference scheme is introduced to improve the dynamic response of on-line learning controllers. The proposed learning algorithm and architectures are studied on three practical problems; (i) Classification of handwritten digits using Fourier Descriptors; (ii) Recognition of underwater targets from sonar returns, considering temporal dependencies of consecutive returns and (iii) On-line learning control of autonomous underwater vehicles, starting with random initial conditions. Detailed studies are conducted on the learning control applications. Effect of the network learning rate on the tracking performance and dynamic response of the system are investigated. Also, the ability of the neural network controllers to adapt to slow and sudden varying parameter disturbances and measurement noise is studied in detail.
Show less - Date Issued
- 1993
- PURL
- http://purl.flvc.org/fcla/dt/12325
- Subject Headings
- Computer algorithms, Computer networks, Neural networks (Computer science), Machine learning
- Format
- Document (PDF)
- Title
- REAL-TIME HIGHWAY TRAFFIC FLOW AND ACCIDENT SEVERITY PREDICTION IN VEHICULAR NETWORKS USING DISTRIBUTED MACHINE LEARNING AND BIG DATA ANALYSIS.
- Creator
- Alnami, Hani Mohammed, Mahgoub, Imadeldin, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
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In recent years, Florida State recorded thousands of abnormal traffic flows on highways that were caused by traffic incidents. Highway traffic congestion costed the US economy 101 billion dollars in 2020. Therefore, it is imperative to develop effective real-time traffic flow prediction schemes to mitigate the impact of traffic congestion. In this dissertation, we utilized real-life highway segment-based traffic and incident data obtained from Florida Department of Transportation (FDOT) for...
Show moreIn recent years, Florida State recorded thousands of abnormal traffic flows on highways that were caused by traffic incidents. Highway traffic congestion costed the US economy 101 billion dollars in 2020. Therefore, it is imperative to develop effective real-time traffic flow prediction schemes to mitigate the impact of traffic congestion. In this dissertation, we utilized real-life highway segment-based traffic and incident data obtained from Florida Department of Transportation (FDOT) for real-time incident prediction. We used eight years of FDOT real-life traffic and incident data for Florida I-95 highway to build prediction models for traffic accident severity. Accurate severity prediction is beneficial for responders since it allows the emergency center to dispatch the right number of vehicles without wasting additional resources.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00014089
- Subject Headings
- Traffic flow, Traffic accidents, Machine learning, Big data, Traffic estimation
- Format
- Document (PDF)
- Title
- CRACKING THE SPARSE CODE: LATERAL COMPETITION FORMS ROBUST V1-LIKE REPRESENTATIONS IN CONVOLUTIONAL NEURAL NETWORKS.
- Creator
- Teti, Michael, Barenholtz, Elan, Hahn, William, Florida Atlantic University, Center for Complex Systems and Brain Sciences, Charles E. Schmidt College of Science
- Abstract/Description
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Although state-of-the-art Convolutional Neural Networks (CNNs) are often viewed as a model of biological object recognition, they lack many computational and architectural motifs that are postulated to contribute to robust perception in biological neural systems. For example, modern CNNs lack lateral connections, which greatly outnumber feed-forward excitatory connections in primary sensory cortical areas and mediate feature-specific competition between neighboring neurons to form robust,...
Show moreAlthough state-of-the-art Convolutional Neural Networks (CNNs) are often viewed as a model of biological object recognition, they lack many computational and architectural motifs that are postulated to contribute to robust perception in biological neural systems. For example, modern CNNs lack lateral connections, which greatly outnumber feed-forward excitatory connections in primary sensory cortical areas and mediate feature-specific competition between neighboring neurons to form robust, sparse representations of sensory stimuli for downstream tasks. In this thesis, I hypothesize that CNN layers equipped with lateral competition better approximate the response characteristics and dynamics of neurons in the mammalian primary visual cortex, leading to increased robustness under noise and/or adversarial attacks relative to current robust CNN layers. To test this hypothesis, I develop a new class of CNNs called LCANets, which simulate recurrent, feature-specific lateral competition between neighboring neurons via a sparse coding model termed the Locally Competitive Algorithm (LCA). I first perform an analysis of the response properties of LCA and show that sparse representations formed by lateral competition more accurately mirror response characteristics of primary visual cortical populations and are more useful for downstream tasks like object recognition than previous sparse CNNs, which approximate competition with winner-take-all mechanisms implemented via thresholding.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00014050
- Subject Headings
- Neural networks (Computer science), Machine learning, Computer vision
- Format
- Document (PDF)
- Title
- EMBEDDING LEARNING FOR COMPLEX DYNAMIC INFORMATION NETWORKS.
- Creator
- Wu, Man, Zhu, Xingquan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
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With the rapid development of networking platforms and data intensive applications, networks (or graphs) are becoming convenient and fundamental tools to model the complex inter-dependence among big scale data. As a result, networks (or graphs) are being widely used in many applications, including citation networks [40], social media networks [71], and so on. However, the high complexity (containing many important information) as well as the dynamic nature of the network makes the graph...
Show moreWith the rapid development of networking platforms and data intensive applications, networks (or graphs) are becoming convenient and fundamental tools to model the complex inter-dependence among big scale data. As a result, networks (or graphs) are being widely used in many applications, including citation networks [40], social media networks [71], and so on. However, the high complexity (containing many important information) as well as the dynamic nature of the network makes the graph learning task more difficult. To have better graph representations (capture both node content and graph structure), many research efforts have been made to develop reliable and efficient algorithms. Therefore, the good graph representation learning is the key factor in performing well on downstream tasks. The dissertation mainly focuses on the graph representation learning, which aims to embed both structure and node content information of graphs into a compact and low dimensional space for a new representation learning. More specifically, in order to achieve an efficient and robust graph representation, the following four problems will be studied from different perspectives: 1) We study the problem of positive unlabeled graph learning for network node classification, and present a new deep learning model as a solution; 2) We formulate a new open-world learning problem for graph data, and propose an uncertain node representation learning approach and sampling strategy to solve the problem; 3) For cross-domain graph learning, we present a novel unsupervised graph domain adaptation problem, and propose an effective graph convolutional network algorithm to solve it; 4) We consider a dynamic graph as a network with changing nodes and edges in temporal order and propose a temporal adaptive aggregation network (TAAN) for dynamic graph learning. Finally, the proposed models are verified and evaluated on various real-world datasets.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00014066
- Subject Headings
- Neural networks (Computer science), Machine learning, Graphs, Embeddings (Mathematics)
- Format
- Document (PDF)
- Title
- FEDERATED LEARNING FOR MEDICAL IMAGE CLASSIFICATION.
- Creator
- Blazanovic, Danica, Zhu, Xingquan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
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Machine learning (ML) has traditionally been used to make predictive models by training on local data. However, due to concerns regarding privacy, it is not always possible to collect and combine data from different sources. On the other hand, if there are insufficient data available, it might not be possible to construct accurate models to produce meaningful outcomes. This is where Federated Learning comes to the rescue. Federated Learning (FL) represents a sophisticated distributed machine...
Show moreMachine learning (ML) has traditionally been used to make predictive models by training on local data. However, due to concerns regarding privacy, it is not always possible to collect and combine data from different sources. On the other hand, if there are insufficient data available, it might not be possible to construct accurate models to produce meaningful outcomes. This is where Federated Learning comes to the rescue. Federated Learning (FL) represents a sophisticated distributed machine learning strategy that enables multiple devices hosted at different institutions such as hospitals, to collaboratively train a global model while ensuring that their respective data remains securely stored on-premises. It addresses privacy concerns and data protection regulations, because raw data does not need to be shared or centralized during the training process. This thesis research studies how two different FL architectures, centralized and decentralized FL, affect medical image classification. To study and validate the findings, skin cancer images dataset is used in a federated learning setting with five sites/clients, and a center for centralized FL. Experimental results show that using both centralized and decentralized (peer to peer) version of FL for classification of skin cancer images outperforms using the traditional ML. In addition, two different FL settings, centralized federated learning (CFL) and decentralized federated learning (DFL), are compared using different data distributions across sites/clients. Our study shows that the best accuracy (95.14%) was achieved with the DFL model when tested on the original dataset (without adding bias to the class distributions). This asserts that class distribution imbalance between sites has a significant impact to the federated learning.
Show less - Date Issued
- 2023
- PURL
- http://purl.flvc.org/fau/fd/FA00014205
- Subject Headings
- Medical imaging, Diagnostic Imaging--classification, Machine learning
- Format
- Document (PDF)
