RUcore: Rutgers University Community Repository
Determinig the efficacy of mathematical programming approaches for multi-group classification
Descriptive
TypeOfResource
Text
TitleInfo
(ID = T-1)
Title
Determinig the efficacy of mathematical programming approaches for multi-group classification
SubTitle
PartName
PartNumber
NonSort
Identifier
(displayLabel = );
(invalid = )
ETD_1960
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10002600001.ETD.000051644
Language
(objectPart = )
LanguageTerm
(authority = ISO639-2);
(type = code)
eng
Genre
(authority = marcgt)
theses
Subject
(ID = SBJ-1);
(authority = RUETD)
Topic
Management
Abstract
Managers have been grappling with the problem of extracting patterns out of the vast database generated by their systems. The advent of powerful information systems in organizations and the consequent agglomeration of vast pool of data since the mid-1980s have created renewed interest in the usefulness of discriminant analysis (DA). Expert systems have come to the aid of managers in their day-to-day decision making with many successful applications in financial planning, sales management, and other areas of business operations (Erenguc and Koehler 1990).
Currently, no comprehensive research study exists that tests the robustness of multi-group classification analysis. Our research aims to bridge the gaps in the existing works and take a step further by extending our study to four-group classification problems. The main purpose of this research is to determine the efficacy of mathematical programming classification models, more specifically, LP methods vis-à-vis statistical approaches such as discriminant analysis (Mahalanobis) and logistic regression, an artificial intelligence (AI) technique such as a neural network, and a non-parametric technique such as k-nearest neighborhood (k-NN) for four-group classification problems. This research also proposes an integrated (hybrid) model that combines a non-parametric classification technique and a LP approach to enhance the overall classification performance. Furthermore, the study extends an existing two-group LP model (Bal et al. 2006) based on the work of (Lam and Moy 1996b) and apply it to four-group classification problems. These models are tested through robust computational experiments under varying data conditions using a financial product example. The characteristics of a real dataset are used to simulate (Monte Carlo method) multiple sample runs for four group classification problems with three continuous independent variables.
The experimental results show that LP approaches in general and the proposed integrated method in particular consistently have lower misclassification rates for most data characteristics. Furthermore, the integrated method utilizes the strengths of both the methods: k-NN and linear programming, thereby considerably improving the classification accuracy.
Currently, no comprehensive research study exists that tests the robustness of multi-group classification analysis. Our research aims to bridge the gaps in the existing works and take a step further by extending our study to four-group classification problems. The main purpose of this research is to determine the efficacy of mathematical programming classification models, more specifically, LP methods vis-à-vis statistical approaches such as discriminant analysis (Mahalanobis) and logistic regression, an artificial intelligence (AI) technique such as a neural network, and a non-parametric technique such as k-nearest neighborhood (k-NN) for four-group classification problems. This research also proposes an integrated (hybrid) model that combines a non-parametric classification technique and a LP approach to enhance the overall classification performance. Furthermore, the study extends an existing two-group LP model (Bal et al. 2006) based on the work of (Lam and Moy 1996b) and apply it to four-group classification problems. These models are tested through robust computational experiments under varying data conditions using a financial product example. The characteristics of a real dataset are used to simulate (Monte Carlo method) multiple sample runs for four group classification problems with three continuous independent variables.
The experimental results show that LP approaches in general and the proposed integrated method in particular consistently have lower misclassification rates for most data characteristics. Furthermore, the integrated method utilizes the strengths of both the methods: k-NN and linear programming, thereby considerably improving the classification accuracy.
PhysicalDescription
Form
(authority = gmd)
electronic resource
Extent
ix, 98 p. : ill.
InternetMediaType
application/pdf
InternetMediaType
text/xml
Note
Supplementary File: Title Page
Note
Supplementary File: Abstract, TOC and Others
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references (p. 83-97)
Note
(type = statement of responsibility)
by Dinesh R. Pai
Name
(ID = NAME-1);
(type = personal)
NamePart
(type = family)
Pai
NamePart
(type = given)
Dinesh R.
NamePart
(type = date)
1971
Role
RoleTerm
(authority = RULIB);
(type = )
author
DisplayForm
Dinesh R. Pai
Name
(ID = NAME-2);
(type = personal)
NamePart
(type = family)
Lawrence
NamePart
(type = given)
Kenneth
Role
RoleTerm
(authority = RULIB);
(type = )
chair
Affiliation
Advisory Committee
DisplayForm
Kenneth D Lawrence
Name
(ID = NAME-3);
(type = personal)
NamePart
(type = family)
Armstrong
NamePart
(type = given)
Ronald
Role
RoleTerm
(authority = RULIB);
(type = )
internal member
Affiliation
Advisory Committee
DisplayForm
Ronald D Armstrong
Name
(ID = NAME-4);
(type = personal)
NamePart
(type = family)
Lawrence
NamePart
(type = given)
Sheila
Role
RoleTerm
(authority = RULIB);
(type = )
internal member
Affiliation
Advisory Committee
DisplayForm
Sheila M Lawrence
Name
(ID = NAME-5);
(type = personal)
NamePart
(type = family)
Klimberg
NamePart
(type = given)
Ronald
Role
RoleTerm
(authority = RULIB);
(type = )
outside member
Affiliation
Advisory Committee
DisplayForm
Ronald K Klimberg
Name
(ID = NAME-1);
(type = corporate)
NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB);
(type = )
degree grantor
Name
(ID = NAME-2);
(type = corporate)
NamePart
Graduate School - Newark
Role
RoleTerm
(authority = RULIB);
(type = )
school
OriginInfo
DateCreated
(point = );
(qualifier = exact)
2009
DateOther
(qualifier = exact);
(type = degree)
2009-10
Place
PlaceTerm
(type = code)
xx
RelatedItem
(type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
RelatedItem
(type = host)
TitleInfo
Title
Graduate School - Newark Electronic Theses and Dissertations
Identifier
(type = local)
rucore10002600001
Location
PhysicalLocation
(authority = marcorg);
(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T3PR7W60
Genre
(authority = ExL-Esploro)
ETD doctoral
Back to the top
Rights
RightsDeclaration
(AUTHORITY = GS);
(ID = rulibRdec0006)
The author owns the copyright to this work
Copyright
Status
Copyright protected
Notice
Note
Availability
Status
Open
Reason
Permission or license
Note
RightsHolder
(ID = PRH-1);
(type = personal)
Name
FamilyName
Pai
GivenName
Dinesh
Role
Copyright holder
RightsEvent
(ID = RE-1);
(AUTHORITY = rulib)
Type
Permission or license
Label
Place
DateTime
Detail
AssociatedEntity
(ID = AE-1);
(AUTHORITY = rulib)
Role
Copyright holder
Name
Dinesh Pai
Affiliation
Rutgers University. Graduate School - Newark
AssociatedObject
(ID = AO-1);
(AUTHORITY = rulib)
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.
Back to the top
Technical
An error occurred while attempting to load this metadata section
Back to the top
Statistical Profile