A hybrid approach for feature subset selection using ant colony optimization and artificial neural networks

Author

Rahul Karthik Sivagaminathan

Abstract

"Feature selection deals with selecting a subset of feature from a data set to predict the output with an acceptable level of accuracy. Feature selection problems have been solved previously by researchers using various meta-heuristic algorithms like branch and bound method, genetic algorithm, simulated annealing etc. This thesis presents a hybrid approach using artificial neural network and ant colony optimization, which would find out the inter-variable relationship amongst a subset of feature, if any, to predict the output accurately"--Abstract, page iv.

Advisor(s)

Ramakrishnan, Sreeram

Committee Member(s)

Dagli, Cihan H., 1949-
Enke, David Lee, 1965-

Department(s)

Engineering Management and Systems Engineering

Degree Name

M.S. in Systems Engineering

Publisher

University of Missouri--Rolla

Publication Date

Spring 2007

Journal article titles appearing in thesis/dissertation

• Modified hybrid approach for feature selection using ant colony optimization and neural networks

Pagination

xii, 96 pages

Rights

© 2007 Rahul Karthik Sivagaminathan, All rights reserved.

Document Type

Thesis - Citation

File Type

text

Language

English

Subject Headings

Ants -- Behavior -- Mathematical modelsMathematical optimizationNeural networks (Computer science)

Thesis Number

T 9145

Print OCLC #

173405216

Recommended Citation

Sivagaminathan, Rahul Karthik, "A hybrid approach for feature subset selection using ant colony optimization and artificial neural networks" (2007). Masters Theses. 4452.
https://scholarsmine.mst.edu/masters_theses/4452