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An Improved BPSO Algorithm for Feature Selection

  • Lalit Kumar
  • Kusum Kumari Bharti
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 524)

Abstract

In machine learning and data mining tasks, feature selection has been used to select the relevant subset of features. Traditionally, high-dimensional datasets have so many redundant and irrelevant features, which degrade the performance of clustering. Therefore, feature selection is necessary to improve the clustering performance. In this paper, we select the optimal subset of features and perform cluster analysis simultaneously using modified-BPSO (Binary Particle Swarm Optimization) and K-means. Optimality of clusters is measured by various cluster validation indices. By comparing the overall performance of the modified-BPSO with the BPSO and BMFOA (Binary Moth Flame Optimization Algorithm) on six real datasets drawn from the UC Irvine Machine Learning Repository, the results show that the performance of the proposed method is better than other methods involved in the paper.

Keywords

BPSO BMFO Cluster validation index Data clustering Feature selection Swarm intelligence 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Design and ManufacturingPDPM-Indian Institute of Information TechnologyJabalpurIndia

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