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This paper proposes two wrapper based feature selection approaches, which are single feature ranking and binary particle swarm optimisation (BPSO) based feature subset ranking. In the first approach, individual features are ranked according to the classification accuracy so that feature selection can be accomplished by using only a few top-ranked features for classification. In the second approach, BPSO is applied to feature subset ranking to search different feature subsets. K-nearest neighbour (KNN) with n-fold cross-validation is employed to evaluate the classification accuracy on eight datasets in the experiments. Experimental results show that using a relatively small number of the top-ranked features obtained from the first approach or one of the top-ranked feature subsets obtained from the second approach can achieve better classification performance than using all features. BPSO could efficiently search for subsets of complementary features to avoid redundancy and noise. Compared with linear forward selection (LFS) and greedy stepwise backward selection (GSBS), in almost all cases, the two proposed approaches could achieve better performance in terms of classification accuracy and the number of features. The BPSO based approach outperforms single feature ranking approach for all the datasets.