A hybrid approach for feature subset selection using ant colony optimization and artificial neural networks
"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.
Dagli, Cihan H., 1949-
Enke, David Lee, 1965-
Engineering Management and Systems Engineering
M.S. in Systems Engineering
University of Missouri--Rolla
Journal article titles appearing in thesis/dissertation
- Modified hybrid approach for feature selection using ant colony optimization and neural networks
xii, 96 leaves
© 2007 Rahul Karthik Sivagaminathan, All rights reserved.
Thesis - Citation
Library of Congress Subject Headings
Ants -- Behavior -- Mathematical models
Neural networks (Computer science)
Print OCLC #
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
Sivagaminathan, Rahul Karthik, "A hybrid approach for feature subset selection using ant colony optimization and artificial neural networks" (2007). Masters Theses. 4452.