NOTE: This item is not available outside the Texas A&M University network. Texas A&M affiliated users who are off campus can access the item through NetID and password authentication or by using TAMU VPN. Non-affiliated individuals should request a copy through their local library's interlibrary loan service.
Enhancing inductive learning with feature selection and example selection
dc.creator | Raman, Baranidharan | |
dc.date.accessioned | 2012-06-07T23:21:10Z | |
dc.date.available | 2012-06-07T23:21:10Z | |
dc.date.created | 2003 | |
dc.date.issued | 2003 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/ETD-TAMU-2003-THESIS-R37 | |
dc.description | Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item. | en |
dc.description | Includes bibliographical references (leaves 59-66). | en |
dc.description | Issued also on microfiche from Lange Micrographics. | en |
dc.description.abstract | While most of the stable learning algorithms perform well on domains with relevant information, they degrade in the presence of irrelevant or redundant information. Selective or focused learning presents a solution to this problem. Two components of selective learning are selective attention (feature selection) and selective utilization (example selection). In this thesis, we present novel algorithms for feature selection and example selection and present the benefits of these two approaches independently and as a combined scheme. We propose a sequential search filter approach called Subset selection using Case-based Relevance APproach (SCRAP) for identifying and eliminating irrelevant features. The SCRAP filter addresses the problem of finding a feature subset that provides a balance between defining consistent hypotheses and improving prediction accuracy. The SCRAP filter was compared with the RELIEF filter algorithm and was found to perform better on three families of learning algorithms. We also propose the Learning Algorithm using SEarch Ring (LASER) framework to perform example selection for learning algorithms. The LASER framework has two components, an example selection scheme and target learner. Naive Bayes was used as the target learner for our experiments. LASER provides significant improvement in prediction accuracy of the naive Bayes learner compared to the naive Bayes classifier without example selection. Application of both feature and example selection schemes to the naive Bayes learner resulted in better prediction accuracy. | en |
dc.format.medium | electronic | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.publisher | Texas A&M University | |
dc.rights | This thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries in 2008. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use. | en |
dc.subject | computer science. | en |
dc.subject | Major computer science. | en |
dc.title | Enhancing inductive learning with feature selection and example selection | en |
dc.type | Thesis | en |
thesis.degree.discipline | computer science | en |
thesis.degree.name | M.S. | en |
thesis.degree.level | Masters | en |
dc.type.genre | thesis | en |
dc.type.material | text | en |
dc.format.digitalOrigin | reformatted digital | en |
Files in this item
This item appears in the following Collection(s)
-
Digitized Theses and Dissertations (1922–2004)
Texas A&M University Theses and Dissertations (1922–2004)
Request Open Access
This item and its contents are restricted. If this is your thesis or dissertation, you can make it open-access. This will allow all visitors to view the contents of the thesis.