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Optimization in logical analysis of data
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Title
Optimization in logical analysis of data
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Bonates
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Tiberius
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Tiberius Bonates
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Boros
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Endre Boros
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chair
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Gurvich
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Vladimir
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Vladimir Gurvich
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Kogan
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Alexander
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Alexander Kogan
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Prekopa
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Andras
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Advisory Committee
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Andras Prekopa
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Alexe
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Gabriela
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Advisory Committee
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Gabriela Alexe
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outside member
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Maculan
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Nelson
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Advisory Committee
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Nelson Maculan
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Rutgers University
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Graduate School - New Brunswick
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theses
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2007
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2007
Language
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English
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electronic
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x, 105 pages
Abstract
Logical Analysis of Data (LAD) is a machine learning/data mining methodology that combines ideas from areas such as Boolean functions, optimization and logic. In this thesis, we focus on the description and the application of novel optimization models to the construction of improved and/or simplified LAD models of data. We address the construction of LAD classification models, proposing two alternative ways of generating patterns, or rules. First, we show how to construct LAD models based on patterns of maximum coverage. We show, through a series of computational experiments, that such models are as good as, if not better than those obtained with the standard LAD implementation and other machine learning methods, while requiring a much simpler calibration for optimal performance. We formulate the problem of finding the most suitable LAD model as a large linear program, and show how to solve it using column generation. For the subproblem phase, we describe a branch-and-bound algorithm, whose performance is significantly superior to that of a commercial integer programming solver. The
LAD models produced by this algorithm are virtually parameter-free and practically as accurate as the calibrated models obtained with other machine learning methods. Finally, we propose a novel regression algorithm that extends the LAD methodology for the case of a numerical outcome and show that it constitutes an attractive alternative to other regression methods in terms of performance and flexibility of use.
LAD models produced by this algorithm are virtually parameter-free and practically as accurate as the calibrated models obtained with other machine learning methods. Finally, we propose a novel regression algorithm that extends the LAD methodology for the case of a numerical outcome and show that it constitutes an attractive alternative to other regression methods in terms of performance and flexibility of use.
Note
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Ph.D.
Note
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Includes bibliographical references (p. 95-103).
Subject
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Topic
Operations Research
Subject
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Topic
Data mining
Subject
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Topic
Machine learning
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.15788
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ETD_420
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Identifier
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doi:10.7282/T32N52PZ
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ETD doctoral
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Rights
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The author owns the copyright to this work.
Copyright
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Copyright protected
Availability
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Open
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Name
Tiberius Bonates
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Affiliation
Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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