Attribute selection method based on a hybrid BPNN and PSO algorithms

Authors:
Cong Jin;Shu-Wei Jin;Li-Na Qin
Affiliations:
Department of Computer Science, Central China Normal University, Wuhan 430079, PR China;Faculté des Sciences et Technologies, Université Claude Bernard Lyon 1, Bítiment Gabriel Lippmann, 14, rue Enrico Fermi, 69622 Villeurbanne Cedex, France;Department of Computer Science, Central China Normal University, Wuhan 430079, PR China
Venue:
Applied Soft Computing
Year:
2012

Citing 14
Cited 1

Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Swarm intelligence

Swarm intelligence
Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
Feature Subset Selection Using a Genetic Algorithm

IEEE Intelligent Systems
A hybrid approach for feature subset selection using neural networks and ant colony optimization

Expert Systems with Applications: An International Journal
A hybrid genetic algorithm for feature selection wrapper based on mutual information

Pattern Recognition Letters
Particle swarm optimization for parameter determination and feature selection of support vector machines

Expert Systems with Applications: An International Journal
A wrapper method for feature selection using Support Vector Machines

Information Sciences: an International Journal
Combination of modified BPNN algorithms and an efficient feature selection method for text categorization

Information Processing and Management: an International Journal
Neural Network Classifier with Entropy Based Feature Selection on Breast Cancer Diagnosis

Journal of Medical Systems
Learning decision rules for pattern classification under a family of probability measures

IEEE Transactions on Information Theory
Learning pattern classification-a survey

IEEE Transactions on Information Theory
A new pruning heuristic based on variance analysis of sensitivity information

IEEE Transactions on Neural Networks
Input feature selection for classification problems

IEEE Transactions on Neural Networks

Genetic algorithm-based heuristic for feature selection in credit risk assessment

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

High dimensional data contain many redundant or irrelevant attributes, which will be difficult for data mining and a variety of pattern recognition. When implementing data mining or a variety of pattern recognition on high dimensional space, it is necessary to reduce the dimension of high dimensional space. In this paper, a new attribute importance measure and selection methods based on attribute ranking was proposed. In proposed attribute selection method, input output correlation (IOC) is applied for calculating attribute' importance, and then sorts them according to descending order. The hybrid of Back Propagation Neural Network (BPNN) and Particle Swarm Optimization (PSO) algorithms is also proposed. PSO is used to optimize weights and thresholds of BPNN for overcoming the inherent shortcoming of BPNN. The experiment results show the proposed attribute selection method is an effective preproceesing technology.