AN EMPIRICAL STUDY OF THE SUITABILITY OF CLASS DECOMPOSITION FOR LINEAR CLASSIFIERS

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dc.contributor.advisorVilalta, Ricardo
dc.contributor.committeeMemberHuang, Stephen
dc.contributor.committeeMemberCheng, Kam-Hoi
dc.contributor.committeeMemberDeng, Zhigang
dc.contributor.committeeMemberKaiser, Klaus
dc.creatorOcegueda-Hernandez, Francisco 1978-
dc.date.accessioned2014-12-09T13:26:57Z
dc.date.available2014-12-09T13:26:57Z
dc.date.createdDecember 2012
dc.date.issued2012-12
dc.date.updated2014-12-09T13:26:57Z
dc.description.abstractThe presence of sub-classes within a data sample suggests a class decomposition approach to classification, where each subclass is treated as a new class. Class decomposition can be effected using multiple linear classifiers in an attempt to outperform a single global linear classifier; the goal is to gain in model complexity while keeping error variance low. In this dissertation, we propose a study aimed at understanding the conditions behind the success or failure of class decomposition when combined with linear classifiers. We identify two relevant data properties as indicators of the suitability of class decomposition: 1) linear separability; and 2) class overlap. We use well-known data complexity measures to evaluate the presence of these properties in a data sample. Our methodology indicates when to avoid performing class decomposition based on such data properties. In addition we conduct a similar analysis at a more granular level for data samples marked as suitable for class decomposition. This extra analysis shows how to improve in efficiency during class decomposition. From an empirical standpoint, we test our technique on several real-world classification problems; results validate our methodology.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10657/798
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectMeta-learning
dc.subjectModel Selection in Classification
dc.subject.lcshComputer science
dc.titleAN EMPIRICAL STUDY OF THE SUITABILITY OF CLASS DECOMPOSITION FOR LINEAR CLASSIFIERS
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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