Abstract
This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric structure of a membership function. Searching for the (sub) optimal subset of features is an NP-hard problem. In this paper, a binary swallow swarm optimization (BSSO) algorithm for feature selection is proposed. To solve the classification problem, we use a fuzzy rule-based classifier. To evaluate the feature selection performance of our method, BSSO is compared to induction without feature selection and some similar algorithms on well-known benchmark datasets. Experimental results show the promising behavior of the proposed method in the optimal selection of features.
Highlights
Introduction and Literature ReviewFeature selection implies extracting a subset of features from an initial set, with these features being fully relevant to a problem at hand or the training problem
We present a novel method for feature selection based on binary swallow swarm optimization (BSSO)
BSSO was compared with two other representative methods, wrapper feature selection based on a random search algorithm (RS), feature selection algorithm based on mutual information (IG), and algorithm without feature selection (All features) on 30 benchmark datasets
Summary
Feature selection implies extracting a subset of features from an initial set, with these features being fully relevant to a problem at hand or the training problem. The reason is that, with increasing number of features, it is necessary to increase the amount of training data, which are required to generate classification rules. Filters are based on the generalized properties of training data and do not use any classifier construction algorithm in the process of feature selection. The advantages of this approach are relatively low computational complexity, sufficient generalization capability, and independence from the classifier. We present a novel method for feature selection based on binary swallow swarm optimization (BSSO). A novel feature selection method based on binary swallow swarm optimization is proposed.
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