Feature Selection Method Based on Parallel Binary Immune Quantum-Behaved Particle Swarm Optimization

Abstract:

In order to enhance the operating speed and reduce the occupied memory space and filter out irrelevant or lower degree of features, feature selection algorithms must be used. However, most of existing feature selection methods are serial and are inefficient timely to be applied to massive text data sets, so it is a hotspot how to improve efficiency of feature selection by means of parallel thinking. This paper presented a feature selection method based on Parallel Binary Immune Quantum-Behaved Particle Swarm Optimization (PBIQPSO). The presented method uses the Binary Immune Quantum-Behaved Particle Swarm Optimization to select feature subset, takes advantage of multiple computing nodes to enhance time efficiency, so can acquire quickly the feature subsets which are more representative. Experimental results show that the method is effective.

• Periodical: Advanced Materials Research (Volumes 546-547)
• Pages: 1538-1543
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.546-547.1538
• Online since: July 2012
• Authors: Chao Chen, Hao Dong Zhu
• Keywords: Binary Immune Quantum-Behaved Particle Swarm Optimization (PBIQPSO), Feature Selection, Feature Space, Feature Subset, Parallel Computing Technique

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