A group evaluation based binary PSO algorithm for feature selection in high dimensional data
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RESEARCH PAPER
A group evaluation based binary PSO algorithm for feature selection in high dimensional data Ramesh Kumar Huda1 · Haider Banka1 Received: 24 June 2019 / Revised: 23 June 2020 / Accepted: 23 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This paper proposes a group evolution based feature selection technique using Binary PSO, which is an essential tool of preprocessing for solving classification problem. A new updating mechanism for calculating Pbest and Gbest are also proposed and the relevance and redundancy of the selected feature subsets are considered as an objective function. The proposed algorithm is tested and compared with four existing feature selection algorithms. In this study, a decision tree classifier is employed to evaluate the classification accuracy of the selected feature subsets on five benchmark datasets. The result shows that proposed algorithm can be successfully used to improve classification accuracy and to improve stability indices as well. It is also observed that with increased weight on relevance of the function, there is a significant reduction on the cardinality of features and increase in classification accuracy. The existing four algorithms usually select a smaller feature subset while the proposed algorithm can achieves higher classification accuracy on most of the test datasets. Keywords Binary particle swarm optimization · Group evaluation · Feature selection · Stability indices · High dimensional data
1 Introduction Feature selection and classification is an important task in machine learning and pattern recognition, in which classify objects into different groups according to available features information. For example G consisting of n number of features, find a feature subset F consisting of m relevant features, where m
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