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Feature selection techniques and applications in bioinformatics
- Date Issued:
- 2011
- Summary:
- 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 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.
Title: | Feature selection techniques and applications in bioinformatics. |
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109 downloads |
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Name(s): |
Dittman, David College of Engineering and Computer Science Department of Computer and Electrical Engineering and Computer Science |
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Type of Resource: | text | |
Genre: | Electronic Thesis Or Dissertation | |
Date Issued: | 2011 | |
Publisher: | Florida Atlantic University | |
Physical Form: | electronic | |
Extent: | xiii, 132 p. : ill. | |
Language(s): | English | |
Summary: | 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 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. | |
Identifier: | 749897270 (oclc), 3175016 (digitool), FADT3175016 (IID), fau:3697 (fedora) | |
Note(s): |
by David Dittman. Thesis (M.S.C.S.)--Florida Atlantic University, 2011. Includes bibliography. Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web. |
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Subject(s): |
Bioinformatifcs Data mining -- Technological innovations Computational biology Combinatorial group theory Filters (Mathematics) Ranking and selection (Statistics) |
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Held by: | FBoU FAUER | |
Persistent Link to This Record: | http://purl.flvc.org/FAU/3175016 | |
Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
Host Institution: | FAU |