Paper Info
Title
Multi-objective feature subset selection using mRMR based enhanced ant colony optimization algorithm (mRMR-EACO).
Abstract
In this research, we propose a novel method to find the relevant feature subset by using ant colony optimisation minimum-redundancy-maximum-relevance. The proposed approach considers the significance of each feature while reducing the dimensionality. The performance of proposed algorithm has been compared with existing biologically inspired feature subset selection algorithms. Eight datasets have been selected from UCI machine learning repository for experimentation. The experimental results indicate that the presented algorithm out performs the other algorithms in terms of the classification accuracy and feature reduction.
Year
DOI
Venue
2016
10.1080/0952813X.2015.1056240
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
feature subset selection,classification,evolutionary algorithm,swarm intelligence
Ant colony optimization algorithms,Dimensionality reduction,Feature selection,Evolutionary algorithm,Computer science,Swarm intelligence,Artificial intelligence,k-nearest neighbors algorithm,Pattern recognition,Algorithm,Curse of dimensionality,Ant colony,Machine learning
Journal
Volume
Issue
ISSN
28.0
6
0952-813X
Citations 
PageRank 
References 
0
0.34
11
Authors
2
Name
Order
Citations
PageRank
Ayesha Khan100.34
Abdul Rauf Baig212615.82