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Comparison of Data Sampling Approaches for Imbalanced Bioinformatics Data
Title
Comparison of Data Sampling Approaches for Imbalanced Bioinformatics Data.
Creator
Dittman, David, Wald, Randall, Napolitano, Amri E., Graduate College, Khoshgoftaar, Taghi M.
Abstract/Description
Class imbalance is a frequent problem found in bioinformatics datasets. Unfortunately, the minority class is usually also the class of interest. One of the methods to improve this situation is data sampling. There are a number of different data sampling methods, each with their own strengths and weaknesses, which makes choosing one a difficult prospect. In our work we compare three data sampling techniques Random Undersampling, Random Oversampling, and SMOTE on six bioinformatics datasets...
Show moreClass imbalance is a frequent problem found in bioinformatics datasets. Unfortunately, the minority class is usually also the class of interest. One of the methods to improve this situation is data sampling. There are a number of different data sampling methods, each with their own strengths and weaknesses, which makes choosing one a difficult prospect. In our work we compare three data sampling techniques Random Undersampling, Random Oversampling, and SMOTE on six bioinformatics datasets with varying levels of class imbalance. Additionally, we apply two different classifiers to the problem 5-NN and SVM, and use feature selection to reduce our datasets to 25 features prior to applying sampling. Our results show that there is very little difference between the data sampling techniques, although Random Undersampling is the most frequent top performing data sampling technique for both of our classifiers. We also performed statistical analysis which confirms that there is no statistical difference between the techniques. Therefore, our recommendation is to use Random Undersampling when choosing a data sampling technique, because it is less computationally expensive to implement than SMOTE and it also reduces the size of the dataset, which will improve subsequent computational costs without sacrificing classification performance.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00005811
Format
Document (PDF)
A review of the stability of feature selection techniques for bioinformatics data
Title
A review of the stability of feature selection techniques for bioinformatics data.
Creator
Awada, Wael, Khoshgoftaar, Taghi M., Dittman, David, Wald, Randall, Napolitano, Amri E., Graduate College
Date Issued
2013-04-12
PURL
http://purl.flvc.org/fcla/dt/3361293
Subject Headings
Bioinformatics, DNA microarrays, Data mining
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)
Machine learning techniques for alleviating inherent difficulties in bioinformatics data
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.
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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)