Masters Theses

Title

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

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, leaf 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 leaves

Rights

© 2007 Rahul Karthik Sivagaminathan, All rights reserved.

Document Type

Thesis - Citation

File Type

text

Language

English

Library of Congress Subject Headings

Ants -- Behavior -- Mathematical models
Mathematical optimization
Neural networks (Computer science)

Thesis Number

T 9145

Print OCLC #

173405216

Link to Catalog Record

Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.

http://laurel.lso.missouri.edu/record=b5978896~S5

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