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Sensitivity analysis for machine cell formation using mathematical model and computer simulation
Title
Sensitivity analysis for machine cell formation using mathematical model and computer simulation.
Creator
Yelamanchi, Ravi., Florida Atlantic University, Han, Chingping (Jim), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description
This thesis presents a mathematical model for the sensitivity analysis of machine cell formation. Computer programs in C were developed. A statistical simulation model is developed to test and verify the mathematical model. The data for machine cell formation in cellular manufacturing is organized in a machine component chart representing the machining requirements of parts in the product mix. The existing machine cell formation models treat the product mix as deterministic. To study the...
Show moreThis thesis presents a mathematical model for the sensitivity analysis of machine cell formation. Computer programs in C were developed. A statistical simulation model is developed to test and verify the mathematical model. The data for machine cell formation in cellular manufacturing is organized in a machine component chart representing the machining requirements of parts in the product mix. The existing machine cell formation models treat the product mix as deterministic. To study the probabilistic nature of the cellular manufacturing, a sensitivity analysis model is presented. The model optimizes the formation of intercellular material handling cost for the machine cell within the constrains of the probability of the product mixture. The results of the mathematical model is compared with the results of the simulation model. It shows that the probabilistic product mix has a influence on the efficiency of the machine cell and the associated total cost.
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Date Issued
1992
PURL
http://purl.flvc.org/fcla/dt/14848
Subject Headings
Group technology--Simulation methods, Manufacturing processes--Data processing
Format
Document (PDF)
Collabortive filtering using machine learning and statistical techniques
Title
Collabortive filtering using machine learning and statistical techniques.
Creator
Su, Xiaoyuan., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Collaborative filtering (CF), a very successful recommender system, is one of the applications of data mining for incomplete data. The main objective of CF is to make accurate recommendations from highly sparse user rating data. My contributions to this research topic include proposing the frameworks of imputation-boosted collaborative filtering (IBCF) and imputed neighborhood based collaborative filtering (INCF). We also proposed a model-based CF technique, TAN-ELR CF, and two hybrid CF...
Show moreCollaborative filtering (CF), a very successful recommender system, is one of the applications of data mining for incomplete data. The main objective of CF is to make accurate recommendations from highly sparse user rating data. My contributions to this research topic include proposing the frameworks of imputation-boosted collaborative filtering (IBCF) and imputed neighborhood based collaborative filtering (INCF). We also proposed a model-based CF technique, TAN-ELR CF, and two hybrid CF algorithms, sequential mixture CF and joint mixture CF. Empirical results show that our proposed CF algorithms have very good predictive performances. In the investigation of applying imputation techniques in mining incomplete data, we proposed imputation-helped classifiers, and VCI predictors (voting on classifications from imputed learning sets), both of which resulted in significant improvement in classification performance for incomplete data over conventional machine learned classifiers, including kNN, neural network, one rule, decision table, SVM, logistic regression, decision tree (C4.5), random forest, and decision list (PART), and the well known Bagging predictors. The main imputation techniques involved in these algorithms include EM (expectation maximization) and BMI (Bayesian multiple imputation).
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Date Issued
2008
PURL
http://purl.flvc.org/FAU/186301
Subject Headings
Filters (Mathematics), Machine learning, Data mining, Technological innovations, Database management, Combinatorial group theory
Format
Document (PDF)
Classification techniques for noisy and imbalanced data
Title
Classification techniques for noisy and imbalanced data.
Creator
Napolitano, Amri E., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Machine learning techniques allow useful insight to be distilled from the increasingly massive repositories of data being stored. As these data mining techniques can only learn patterns actually present in the data, it is important that the desired knowledge be faithfully and discernibly contained therein. Two common data quality issues that often affect important real life classification applications are class noise and class imbalance. Class noise, where dependent attribute values are...
Show moreMachine learning techniques allow useful insight to be distilled from the increasingly massive repositories of data being stored. As these data mining techniques can only learn patterns actually present in the data, it is important that the desired knowledge be faithfully and discernibly contained therein. Two common data quality issues that often affect important real life classification applications are class noise and class imbalance. Class noise, where dependent attribute values are recorded erroneously, misleads a classifier and reduces predictive performance. Class imbalance occurs when one class represents only a small portion of the examples in a dataset, and, in such cases, classifiers often display poor accuracy on the minority class. The reduction in classification performance becomes even worse when the two issues occur simultaneously. To address the magnified difficulty caused by this interaction, this dissertation performs thorough empirical investigations of several techniques for dealing with class noise and imbalanced data. Comprehensive experiments are performed to assess the effects of the classification techniques on classifier performance, as well as how the level of class imbalance, level of class noise, and distribution of class noise among the classes affects results. An empirical analysis of classifier based noise detection efficiency appears first. Subsequently, an intelligent data sampling technique, based on noise detection, is proposed and tested. Several hybrid classifier ensemble techniques for addressing class noise and imbalance are introduced. Finally, a detailed empirical investigation of classification filtering is performed to determine best practices.
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Date Issued
2009
PURL
http://purl.flvc.org/FAU/369201
Subject Headings
Combinatorial group theory, Data mining, Technological innovations, Decision trees, Machine learning, Filters (Mathematics)
Format
Document (PDF)
Feature selection techniques and applications in bioinformatics
Title
Feature selection techniques and applications in bioinformatics.
Creator
Dittman, David, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Possibly the largest problem when working in bioinformatics is the large amount of data to sift through to find useful information. This thesis shows that the use of feature selection (a method of removing irrelevant and redundant information from the dataset) is a useful and even necessary technique to use in these large datasets. This thesis also presents a new method in comparing classes to each other through the use of their features. It also provides a thorough analysis of the use of...
Show morePossibly the largest problem when working in bioinformatics is the large amount of data to sift through to find useful information. This thesis shows that the use of feature selection (a method of removing irrelevant and redundant information from the dataset) is a useful and even necessary technique to use in these large datasets. This thesis also presents a new method in comparing classes to each other through the use of their features. It also provides a thorough analysis of the use of various feature selection techniques and classifier in different scenarios from bioinformatics. Overall, this thesis shows the importance of the use of feature selection in bioinformatics.
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Date Issued
2011
PURL
http://purl.flvc.org/FAU/3175016
Subject Headings
Bioinformatifcs, Data mining, Technological innovations, Computational biology, Combinatorial group theory, Filters (Mathematics), Ranking and selection (Statistics)
Format
Document (PDF)
Stability analysis of feature selection approaches with low quality data
Title
Stability analysis of feature selection approaches with low quality data.
Creator
Altidor, Wilker., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
One of the greatest challenges to data mining is erroneous or noisy data. Several studies have noted the weak performance of classification models trained from low quality data. This dissertation shows that low quality data can also impact the effectiveness of feature selection, and considers the effect of class noise on various feature ranking techniques. It presents a novel approach to feature ranking based on ensemble learning and assesses these ensemble feature selection techniques in...
Show moreOne of the greatest challenges to data mining is erroneous or noisy data. Several studies have noted the weak performance of classification models trained from low quality data. This dissertation shows that low quality data can also impact the effectiveness of feature selection, and considers the effect of class noise on various feature ranking techniques. It presents a novel approach to feature ranking based on ensemble learning and assesses these ensemble feature selection techniques in terms of their robustness to class noise. It presents a noise-based stability analysis that measures the degree of agreement between a feature ranking techniques output on a clean dataset versus its outputs on the same dataset but corrupted with different combinations of noise level and noise distribution. It then considers classification performances from models built with a subset of the original features obtained after applying feature ranking techniques on noisy data. It proposes the focused ensemble feature ranking as a noise-tolerant approach to feature selection and compares focused ensembles with general ensembles in terms of the ability of the selected features to withstand the impact of class noise when used to build classification models. Finally, it explores three approaches for addressing the combined problem of high dimensionality and class imbalance. Collectively, this research shows the importance of considering class noise when performing feature selection.
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Date Issued
2011
PURL
http://purl.flvc.org/FAU/3174501
Subject Headings
Data mining, Technological innovations, Combinatorial group theory, Filters (Mathematics), Ranking and selection (Statistics)
Format
Document (PDF)
system for assisting in the determination of geometric similarity between machined cylindrical parts
Title
A system for assisting in the determination of geometric similarity between machined cylindrical parts.
Creator
Lockard, Alan A. L., Florida Atlantic University, Hoffman, Frederick, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
The costs associated with the design and manufacture of machined components can be significantly reduced by the ability to identify and group similar parts. This activity is generally accomplished by assigning each part a Group Technology code number based on its most significant characteristics. Attempts to accomplish this are hindered by: the relatively small amount of information that can be encoded in a code of manageable length, inconsistencies in human interpretation of design and...
Show moreThe costs associated with the design and manufacture of machined components can be significantly reduced by the ability to identify and group similar parts. This activity is generally accomplished by assigning each part a Group Technology code number based on its most significant characteristics. Attempts to accomplish this are hindered by: the relatively small amount of information that can be encoded in a code of manageable length, inconsistencies in human interpretation of design and manufacturing data, the commitment of resources required to review and encode all candidate components at a facility, and the heuristic nature of determining what constitutes significant similarity for any particular application. These problems are addressed by the development of a system that assists in the determination of similarity by comparing CAD (Computer Aided Design) files, rather than Group Technology codes, in a manufacturing oriented frame-based system.
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Date Issued
1989
PURL
http://purl.flvc.org/fcla/dt/14497
Subject Headings
Computer-aided design, Machine parts, Group technology, Manufacturing processes--Data processing
Format
Document (PDF)
Exploring teachers' perceptions of professional development in virtual learning teams
Title
Exploring teachers' perceptions of professional development in virtual learning teams.
Creator
Purnell, Courtney Paschal., College of Education, Department of Educational Leadership and Research Methodology
Abstract/Description
The demand for virtual education is rapidly increasing due to the proliferation of legislation demanding class size limitations, funding cuts, and school choice across the United States. Virtual education leaders are discovering new ways to enhance and develop teachers to become more efficient and increase quality of learning online. Learning teams are one tool implemented by professional development departments in order to obtain a community of shared best practices and increase professional...
Show moreThe demand for virtual education is rapidly increasing due to the proliferation of legislation demanding class size limitations, funding cuts, and school choice across the United States. Virtual education leaders are discovering new ways to enhance and develop teachers to become more efficient and increase quality of learning online. Learning teams are one tool implemented by professional development departments in order to obtain a community of shared best practices and increase professional learning for teachers. ... The purpose of this exploratory case study was to investigate teachers' perceptions of the contribution of virtual learning teams to their professional development in a completely online K-12 environment. ... Five major themes emerged from the interviews, which were teacher professional development as it relates to student success, collaboration, balance, knowledge gained from being part of a virtual learning team, and teachers' perception of student success.
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Date Issued
2012
PURL
http://purl.flvc.org/FAU/3358964
Subject Headings
Education, Effect of technological innovations on, Educational leadership, School management and organization, Teams in the workplace, Group work in education, Professional learning communities, Mentoring in education, Computer networks
Format
Document (PDF)