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Composite boolean separators for data analysis with applications in computed tomography and gene expression microarray data
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Title
Composite boolean separators for data analysis with applications in computed tomography and gene expression microarray data
Name
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Lozina
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Irina I.
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Irina Lozina
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author
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Kogan
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Alexander
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Advisory Committee
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Alexander Kogan
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chair
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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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internal member
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Shanno
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David
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Advisory Committee
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David Shanno
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internal member
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Boros
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Endre
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Advisory Committee
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Endre Boros
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internal member
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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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Rutgers University
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degree grantor
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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
PhysicalDescription
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electronic
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application/pdf
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Extent
xi, 173 pages
Abstract
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An important topic in machine-learning / data-mining is that of analyzing binary datasets.
A binary dataset consists of a subset of n-vectors (observations) with binary components, each of which has an associated binary outcome (the class of the observation). Clearly, the set of n-vectors and their outcomes represent a partially defined Boolean function.
The central problem of machine-learning / data-mining, the so-called classification problem, consists in finding an "extension" of the partially defined Boolean function closely approximating a hidden ("target") function. Various methods have been developed to solve this and related problems, such as identifying misclassified observations, revealing irrelevant and/or redundant variables, etc.
In this thesis, we propose a new approach to analyzing different problems in machine-learning / data-mining. First, we define a simple procedure for generating artificial Boolean variables, called Composite Boolean Features, and describe an iterative algorithm for generating Boolean functions which agree with the outcomes in a large proportion of the observations in the dataset. We call these functions Composite Boolean Separators (CBSes for short). We then use the idea of CBSes in several ways. In particular, we demonstrate the usefulness of these concepts by showing how the introduction of CBSes can enhance the accuracy of classification systems; we employ CBSes for identifying misclassified observations and examine how deletion of such observations and reversal of their class influence the classification accuracy; we apply the new variables to the attribute selection problem, i.e., to the problem of finding "good" (informative) subsets of the original attributes, or equivalently, identifying "bad" (irrelevant and/or redundant) attributes in the given datasets.
All the results have been tested on eight publicly available datasets and validated by five well-known machine-learning / data-mining techniques. Also, we applied CBSes, along with other techniques, to the analysis of two real-life medical datasets: computed tomography data and breast cancer gene expression microarray data.
The results presented in this thesis demonstrate that for many real-life datasets, the application of CBSes increases the classification accuracy significantly. CBSes also prove useful in the missclassification and attribute selection problems.
A binary dataset consists of a subset of n-vectors (observations) with binary components, each of which has an associated binary outcome (the class of the observation). Clearly, the set of n-vectors and their outcomes represent a partially defined Boolean function.
The central problem of machine-learning / data-mining, the so-called classification problem, consists in finding an "extension" of the partially defined Boolean function closely approximating a hidden ("target") function. Various methods have been developed to solve this and related problems, such as identifying misclassified observations, revealing irrelevant and/or redundant variables, etc.
In this thesis, we propose a new approach to analyzing different problems in machine-learning / data-mining. First, we define a simple procedure for generating artificial Boolean variables, called Composite Boolean Features, and describe an iterative algorithm for generating Boolean functions which agree with the outcomes in a large proportion of the observations in the dataset. We call these functions Composite Boolean Separators (CBSes for short). We then use the idea of CBSes in several ways. In particular, we demonstrate the usefulness of these concepts by showing how the introduction of CBSes can enhance the accuracy of classification systems; we employ CBSes for identifying misclassified observations and examine how deletion of such observations and reversal of their class influence the classification accuracy; we apply the new variables to the attribute selection problem, i.e., to the problem of finding "good" (informative) subsets of the original attributes, or equivalently, identifying "bad" (irrelevant and/or redundant) attributes in the given datasets.
All the results have been tested on eight publicly available datasets and validated by five well-known machine-learning / data-mining techniques. Also, we applied CBSes, along with other techniques, to the analysis of two real-life medical datasets: computed tomography data and breast cancer gene expression microarray data.
The results presented in this thesis demonstrate that for many real-life datasets, the application of CBSes increases the classification accuracy significantly. CBSes also prove useful in the missclassification and attribute selection problems.
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references (p. 149-157).
Subject
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Topic
Operations Research
Subject
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Topic
Tomography
Subject
(authority = ETD-LCSH)
Topic
Data mining
Subject
(authority = ETD-LCSH)
Topic
Gene expression
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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.13481
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ETD_160
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doi:10.7282/T3MG7PXJ
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NjNbRU
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ETD doctoral
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Open
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Irina Lozina
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Rutgers University. Graduate School-New Brunswick
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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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