By Topic

A hybridized approach for feature selection using Ant Colony Optimization and Ant-Miner for classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Rasmy, M.H. ; Oper. Res. & Decision Support Dept., Cairo Univ., Cairo, Egypt ; El-Beltagy, M. ; Saleh, M. ; Mostafa, B.

This work presents an Ant Colony Optimization-based approach to feature selection that works in tandem with an ACO classifier (Ant-Miner) in a wrapper approach to improve the classification accuracy of the Ant-Miner with a small and appropriate feature subset. The objective is to analyze the performance of five ACO algorithms on the feature selection problem and the performance of the proposed FS-ACO/Ant-Miner system when compared to other feature selection for classification algorithms. The experimental results indicate that the hybridized approach performs comparatively well in discriminating input features and also achieves high classification accuracy especially for data sets with higher number of features.

Published in:

Informatics and Systems (INFOS), 2012 8th International Conference on

Date of Conference:

14-16 May 2012