Based on binary particle swarm optimisation (BPSO) and information theory, this paper proposes two new filter feature selection methods for classification problems. The first algorithm is based on BPSO and the mutual information of each pair of features, which determines the relevance and redundancy of the selected feature subset. The second algorithm is based on BPSO and the entropy of each group of features, which evaluates the relevance and redundancy of the selected feature subset. Different weights for the relevance and redundancy in the fitness functions of the two proposed algorithms are used to further improve their performance in terms of the number of features and the classification accuracy. In the experiments, a decision tree (DT) is employed to evaluate the classification accuracy of the selected feature subset on the test sets of four datasets. The results show that with proper weights, two proposed algorithms can significantly reduce the number of features and achieve similar or even higher classification accuracy in almost all cases. The first algorithm usually selects a smaller feature subset while the second algorithm can achieve higher classification accuracy.
Feature selection has the two main objectives of minimising the classification error rate and the number of features. Based on binary particle swarm optimisation (BPSO), we develop two novel multi-objective feature selection frameworks for classification, which are multi-objective binary PSO using the idea of nondominated sorting (NSBPSO) and multi-objective binary PSO using the ideas of crowding, mutation and dominance (CMDBPSO). Four multi-objective feature selection methods are then developed by applying mutual information and entropy as two different filter evaluation criteria in each of the proposed frameworks. The proposed algorithms are examined and compared with a single objective method on eight benchmark data sets. Experimental results show that the proposed multi-objective algorithms can evolve a set of solutions that use a smaller number of features and achieve better classification performance than using all features. In most cases, NSBPSO achieves better results than the single objective algorithm and CMDBPSO outperforms all other methods mentioned above. This work represents the first study on multi-objective BPSO for filter-based feature selection.
Feature selection is a multi-objective problem with the two main conflicting objectives of minimising the number of features and maximising the classification performance. However, most existing feature selection algorithms are single objective and do not appropriately reflect the actual need. There are a small number of multi-objective feature selection algorithms, which are wrapper based and accordingly are computationally expensive and less general than filter algorithms. Evolutionary computation techniques are particularly suitable for multi-objective optimisation because they use a population of candidate solutions and are able to find multiple non-dominated solutions in a single run. However, the two well-known evolutionary multi-objective algorithms, nondominated sorting based multi-objective genetic algorithm II (NSGAII) and strength Pareto evolutionary algorithm 2 (SPEA2) have not been applied to filter based feature selection. In this work, based on NSGAII and SPEA2, we develop two multi-objective, filter based feature selection frameworks. Four multi-objective feature selection methods are then developed by applying mutual information and entropy as two different filter evaluation criteria in each of the two proposed frameworks. The proposed multi-objective algorithms are examined and compared with a single objective method and three traditional methods (two filters and one wrapper) on eight benchmark datasets. A decision tree is employed to test the classification performance. Experimental results show that the proposed multi-objective algorithms can automatically evolve a set of non-dominated solutions that include a smaller number of features and achieve better classification performance than using all features. NSGAII and SPEA2 outperform the single objective algorithm, the two traditional filter algorithms and even the traditional wrapper algorithm in terms of both the number of features and the classification performance in most cases. NSGAII achieves similar performance to SPEA2 for the datasets that consist of a small number of features and slightly better results when the number of features is large. This work represents the first study on NSGAII and SPEA2 for filter feature selection in classification problems with both providing field leading classification performance.
Abstract. Feature selection has two main objectives of maximising the classification performance and minimising the number of features. However, most existing feature selection algorithms are single objective wrapper approaches. In this work, we propose a multi-objective filter feature selection algorithm based on binary particle swarm optimisation (PSO) and probabilistic rough set theory. The proposed algorithm is compared with other five feature selection methods, including three PSO based single objective methods and two traditional methods. Three classification algorithms (naïve bayes, decision trees and k-nearest neighbours) are used to test the generality of the proposed filter algorithm. Experiments have been conducted on six datasets of varying difficulty. Experimental results show that the proposed algorithm can automatically evolve a set of non-dominated feature subsets. In almost all cases, the proposed algorithm outperforms the other five algorithms in terms of both the number of features and the classification performance (evaluated by all the three classification algorithms). This paper presents the first study on using PSO and rough set theory for multi-objective feature selection.
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