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Feature selection with applications to text classification
Descriptive
TitleInfo
Title
Feature selection with applications to text classification
Name
(type = personal)
NamePart
(type = family)
Neu
NamePart
(type = given)
David Joseph
DisplayForm
David Neu
Role
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(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Gurvich
NamePart
(type = given)
Vladimir
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Vladimir Gurvich
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
chair
Name
(type = personal)
NamePart
(type = family)
Boros
NamePart
(type = given)
Endre
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Endre Boros
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Jeong
NamePart
(type = given)
Myong K.
DisplayForm
Myong K. Jeong
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Kantor
NamePart
(type = given)
Paul B.
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Paul B. Kantor
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Chaovalitwongse
NamePart
(type = given)
Wanpracha Art
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Wanpracha Art Chaovalitwongse
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
outside member
Name
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NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
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NamePart
Graduate School - New Brunswick
Role
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(authority = RULIB)
school
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Text
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theses
OriginInfo
DateCreated
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2012
DateOther
(qualifier = exact);
(type = degree)
2012-05
CopyrightDate
(qualifier = exact)
2012
Place
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xx
Language
LanguageTerm
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(type = code)
eng
Abstract
(type = abstract)
Application of a feature selection algorithm to a textual data set can improve the performance of some classifiers. Due to the characteristics, specifically the size, of textual data sets researchers have traditionally relied on a family of simple heuristics to perform feature selection. These heuristics, which in practice are quite effective, use functions of individual feature statistics, that we refer to as feature ranking functions, to order the feature set. We are interested in identifying the most effective feature ranking functions. To do this we begin by defining a feature set evaluation methodology. Traditionally the performance of feature selection algorithms has been measured by comparing the performance of classification algorithms before and after feature selection. Instead, we measure various criteria of the selected feature set itself, including measures of separation, noise, size, and robustness. We demonstrate that many of these criteria are competing, and show how the tools of multicriteria optimization can be employed to rank the performance of feature selection algorithms. Using this methodology we evaluate the performance of a large set of feature ranking functions, including a function that measures the rareness of a feature assuming that relevant and irrelevant documents are generated by two independent stochastic processes. Motivated by the results, we identify the defining characteristics of the functions that are most successful, noting that many of these can be written as ratios of measures of separation to measures of noise. Next we introduce a set of axioms which we believe that feature ranking functions should satisfy, and study the set of these functions that can be represented as a linear combination of some finite set of basis functions. We demonstrate that many of the functions or approximations to the functions that we studied are members of this set. Next consider the set of coefficient vectors of this set and show that it is convex, bounded, and not empty. We conclude by investigating the performance of other approaches to feature selection including greedy and ensemble algorithms that use feature ranking functions.
Subject
(authority = RUETD)
Topic
Operations Research
RelatedItem
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
Identifier
ETD_3989
PhysicalDescription
Form
(authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xiv, 360 p. : ill.
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = vita)
Includes vita
Note
(type = statement of responsibility)
by David Joseph Neu
Subject
(authority = ETD-LCSH)
Topic
Heuristic algorithms--Methodology
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000065235
RelatedItem
(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
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rucore19991600001
Location
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(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T33B5Z39
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
RightsDeclaration
(ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Neu
GivenName
David
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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(qualifier = exact);
(point = start)
2012-04-16 13:49:38
AssociatedEntity
Name
David Neu
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
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.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical
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7744000
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