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Composite boolean separators for data analysis with applications in computed tomography and gene expression microarray data

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TitleInfo
Title
Composite boolean separators for data analysis with applications in computed tomography and gene expression microarray data
Name (type = personal)
NamePart (type = family)
Lozina
NamePart (type = given)
Irina I.
DisplayForm
Irina Lozina
Role
RoleTerm (authority = RUETD)
author
Name (type = personal)
NamePart (type = family)
Kogan
NamePart (type = given)
Alexander
Affiliation
Advisory Committee
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Alexander Kogan
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chair
Name (type = personal)
NamePart (type = family)
Prekopa
NamePart (type = given)
Andras
Affiliation
Advisory Committee
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Andras Prekopa
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RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Shanno
NamePart (type = given)
David
Affiliation
Advisory Committee
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David Shanno
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Boros
NamePart (type = given)
Endre
Affiliation
Advisory Committee
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Endre Boros
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Alexe
NamePart (type = given)
Gabriela
Affiliation
Advisory Committee
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Gabriela Alexe
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School-New Brunswick
Role
RoleTerm (authority = RULIB)
school
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Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2007
DateOther (qualifier = exact); (type = degree)
2007
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
PhysicalDescription
Form (authority = marcform)
electronic
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application/pdf
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text/xml
Extent
xi, 173 pages
Abstract (type = abstract)
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.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references (p. 149-157).
Subject (authority = RUETD)
Topic
Operations Research
Subject (authority = ETD-LCSH)
Topic
Tomography
Subject (authority = ETD-LCSH)
Topic
Data mining
Subject (authority = ETD-LCSH)
Topic
Gene expression
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.13481
Identifier
ETD_160
Identifier (type = doi)
doi:10.7282/T3MG7PXJ
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work.
Copyright
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Open
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Name
Irina Lozina
Role
Copyright holder
Affiliation
Rutgers University. Graduate School-New Brunswick
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Non-exclusive ETD license
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Author Agreement License
Detail
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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