International Journal of Advanced Computing and Communication Systems

Multi Layered Back Propagation – Particle Swarm Optimization Algorithm for Feature Reduction

Prasanka R and Balamuragan M, School of computer science, Engineering and applications, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India.

International Journal of Advanced Computing and Communication Systems

Received On :

Revised On :

Accepted On :

Published On :

Volume 05, Issue 03

Page No :024-027

Abstract

Feature Reduction is an important step in data mining technique which removes unwanted attributes. It is the process of choosing the subset of relevant features for obtaining best accuracy result. The feature selection technique is used in various research fields. In this paper a hybrid algorithm named Multi Layered Back Propagation – Particle Swarm Optimization (MLBP-PSO) Algorithm is proposed by combining the two existing algorithms, Particle Swarm Optimization and Back Propagation Learning Algorithm. The result obtained proves that the proposed algorithm work well for Feature Reduction when comparing with the existing algorithms.

Keywords

Data Mining, Feature Reduction, Particle Swarm Optimization, Multilayer Perception.

Cite this Article

Prasanka R and Balamuragan M, “Multi Layered Back Propagation – Particle Swarm OptimizationAlgorithm for Feature Reduction, ”International Journal of Advanced Computing and Communication Systems, pp. 001-008, November 2018.

Copyright

© 2018 Prasanka R and Balamuragan M. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.