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PSO and Statistical Clustering for Feature Selection: A New Representation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8886)

Abstract

Classification tasks often involve a large number of features, where irrelevant or redundant features may reduce the classification performance. Such tasks typically requires a feature selection process to choose a small subset of relevant features for classification. This paper proposes a new representation in particle swarm optimisation (PSO) to utilise statistical clustering information to solve feature selection problems. The proposed algorithm is examined and compared with two conventional feature selection algorithms and two existing PSO based algorithms on eight benchmark datasets of varying difficulty. The experimental results show that the proposed algorithm can be successfully used for feature selection to considerably reduce the number of features and achieve similar or significantly higher classification accuracy than using all features. It achieves significantly better classification accuracy than one conventional method although the number of features is larger. Compared with the other conventional method and the two PSO methods, the proposed algorithm achieves better performance in terms of both the classification performance and the number of features.

Keywords

Particle swarm optimisation Feature selection Classification Representation 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand
  2. 2.School of Mathematics, Statistics and Operations ResearchVictoria University of WellingtonWellingtonNew Zealand

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