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Relief for regression with missing data in variable selection
Descriptive
TitleInfo
Title
Relief for regression with missing data in variable selection
Name
(type = personal)
NamePart
(type = family)
Li
NamePart
(type = given)
Luquan
NamePart
(type = date)
1989-
DisplayForm
Luquan Li
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Jeong
NamePart
(type = given)
Myong K
DisplayForm
Myong K Jeong
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
chair
Name
(type = personal)
NamePart
(type = family)
Boros
NamePart
(type = given)
Endre
DisplayForm
Endre Boros
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Prekopa
NamePart
(type = given)
Andras
DisplayForm
Andras Prekopa
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal 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
TypeOfResource
Text
Genre
(authority = marcgt)
theses
OriginInfo
DateCreated
(qualifier = exact)
2014
DateOther
(qualifier = exact);
(type = degree)
2014-05
Place
PlaceTerm
(type = code)
xx
Language
LanguageTerm
(authority = ISO639-2b);
(type = code)
eng
Abstract
(type = abstract)
Variable selection is a significant pre-processing task for prediction in the field of data mining and machine learning, and involves the selection of a subset of relevant variables. Almost all researchers have faced the problem of missing data, which can occur due to nonresponse or loss of information. This thesis develops a new variable selection technique for dealing with missing data. Relief is an algorithm for estimating the quality of each variable and is applicable to categorical or continuous data. This thesis presents a new variable selection method, RM-Relief, by extending Relief to select the variables in a regression with missing data. RM-Relief weights all predictor variables by assigning bins for a response variable and estimating the conditional probability of unknown instances. Results on artificial and real-world datasets indicate that RM-Relief works well on regression problems with missing data.
Subject
(authority = RUETD)
Topic
Operations Research
Subject
(authority = ETD-LCSH)
Topic
Data mining
Subject
(authority = ETD-LCSH)
Topic
Machine learning
Subject
(authority = ETD-LCSH)
Topic
Variational principles
RelatedItem
(type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
Identifier
ETD_5453
PhysicalDescription
Form
(authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
iii, 31 p. : ill.
Note
(type = degree)
M.S.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = statement of responsibility)
by Luquan Li
Subject
(authority = ETD-LCSH)
Topic
Regression analysis--Data processing
RelatedItem
(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
(type = local)
rucore19991600001
Location
PhysicalLocation
(authority = marcorg);
(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T3VD6WSB
Genre
(authority = ExL-Esploro)
ETD graduate
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Rights
RightsDeclaration
(ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Li
GivenName
Luquan
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
(encoding = w3cdtf);
(qualifier = exact);
(point = start)
2014-04-11 12:46:19
AssociatedEntity
Name
Luquan Li
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
RULTechMD
(ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem
(VERSION = 5.1)
windows xp
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Statistical Profile