Abstract/Details

Support vector machine /regression feature selection with an application towards classification


2005 2005

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Abstract (summary)

Initial data mining and response surface methodology applications demonstrated support vector machines and regression (SVM and SVR, respectively) as the choice classification function approximation for market data provided by the United States Army Accessions and Recruiting Commands (59), (60). Unlike logistic regression and random forest, SVM/SVR alone cannot provide insight to feature selection, but rather exhibits “black box” functionality similar to artificial neural networks. Motivated by the predictive result of SVM/SVR, this research obtains SVM/SVR features by combining a kernel recursive criterion within an alternate branch and bound enumeration. The kernel recursive criterion eliminates repetitive SVM/SVR calculations, thereby speeding the convergence to an optimal feature subset, which is guaranteed by the alternate enumeration and monotonic property of a quadratic loss function.

We compare the SVM/SVR feature selection results with step-wise logistic regression to show the usefulness of the method. The experiment occurs within a response surface methodology environment using experimental data. We control the number of prediction variables, the number of noise variables, the error on the noise variables, and the training data size. In support of the SVM/SVR feature selection, the research demonstrates a method for deploying the SVM/SVR feature selection on Army recruiting and market data.

The contributions of the research are: (1) developing a SVM/SVR specific kernel recursive criterion within an alternate branch and bound enumeration; (2) assessing the effects of training data size on the recursive kernel criterion: and (3) demonstrating the effectiveness of the kernel recursive criterion for feature selection on an Army market data application.

Indexing (details)


Subject
Systems design;
Statistics
Classification
0790: Systems design
0463: Statistics
Identifier / keyword
Applied sciences; Pure sciences; Classification; Data mining; Feature selection; Response surface; Support vector machine/regression
Title
Support vector machine /regression feature selection with an application towards classification
Author
Halstead, John Brantley
Number of pages
204
Publication year
2005
Degree date
2005
School code
0246
Source
DAI-B 66/05, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780542170645, 0542170647
Advisor
Brown, Donald
University/institution
University of Virginia
University location
United States -- Virginia
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3177523
ProQuest document ID
305406852
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/305406852
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