Hybrid evolutionary algorithms for classification data mining

In this paper, we propose novel methods to find the best relevant feature subset using fuzzy rough set-based attribute subset selection with biologically inspired algorithm search such as ant colony and particle swarm optimization and the principles of an evolutionary process. We then propose a hybr...

Full description

Saved in:
Bibliographic Details
Published in:Neural computing & applications Vol. 26; no. 3; pp. 507 - 523
Main Authors: Panda, Mrutyunjaya, Abraham, Ajith
Format: Journal Article
Language:English
Published: London Springer London 01.04.2015
Subjects:
Advances in Intelligent Data Processing and Analysis
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Image Processing and Computer Vision
Probability and Statistics in Computer Science
Bio-inspired algorithms
Statistical test
Evolutionary algorithms
Neural network
Fuzzy rough
Classification
ISSN:0941-0643, 1433-3058
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we propose novel methods to find the best relevant feature subset using fuzzy rough set-based attribute subset selection with biologically inspired algorithm search such as ant colony and particle swarm optimization and the principles of an evolutionary process. We then propose a hybrid fuzzy rough with K -nearest neighbor (K-NN)-based classifier (FRNN) to classify the patterns in the reduced datasets, obtained from the fuzzy rough bio-inspired algorithm search. While exploring other possible hybrid evolutionary processes, we then conducted experiments considering (i) same feature selection algorithm with support vector machine (SVM) and random forest (RF) classifier; (ii) instance based selection using synthetic minority over-sampling technique with fuzzy rough K -nearest neighbor (K-NN), SVM and RF classifier. The proposed hybrid is subsequently validated using real-life datasets obtained from the University of California, Irvine machine learning repository. Simulation results demonstrate that the proposed hybrid produces good classification accuracy. Finally, parametric and nonparametric statistical tests of significance are carried out to observe consistency of the classifiers.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-014-1673-2