NDLI: New fitness functions in binary particle swarm optimisation for feature selection

Content Provider	IEEE Xplore Digital Library
Author	Bing Xue Mengjie Zhang Browne, W.N.
Copyright Year	2012
Description	Author affiliation: School of Engineering and Computer Science, Victoria University of Wellington, New Zealand (Bing Xue; Mengjie Zhang; Browne, W.N.)
Abstract	Feature selection is an important data preprocessing technique in classification problems. This paper proposes two new fitness functions in binary particle swarm optimisation (BPSO) for feature selection to choose a small number of features and achieve high classification accuracy. In the first fitness function, the relative importance of classification performance and the number of features are balanced by using a linearly increasing weight in the evolutionary process. The second is a two-stage fitness function, where classification performance is optimised in the first stage and the number of features is taken into account in the second stage. K-nearest neighbour (KNN) is employed to evaluate the classification performance in the experiments on ten datasets. Experimental results show that by using either of the two proposed fitness functions in the training process, in almost all cases, BPSO can select a smaller number of features and achieve higher classification accuracy on the test sets than using overall classification performance as the fitness function. They outperform two conventional feature selection methods in almost all cases. In most cases, BPSO with the second fitness function can achieve better performance than with the first fitness function in terms of classification accuracy and the number of features.
Starting Page	1
Ending Page	8
File Size	770967
Page Count	8
File Format	PDF
ISBN	9781467315104
e-ISBN	9781467315098
e-ISBN	9781467315081
DOI	10.1109/CEC.2012.6256617
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-10
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Equations Accuracy Error analysis Training Optimization Mathematical model Educational institutions
Content Type	Text
Resource Type	Article

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