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Classification problems often have a large number of features in the data sets, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the performance. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main conflicting objectives of maximizing...
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...
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