| Summary: | As the volume of data available for analysis grows, feature selection is becoming a vital part of ensuring accurate classification results. In classification problems, selecting a small number of features reduces computational complexity, but selecting the right features is important to maintain a high level of accuracy. In this paper, we present a feature selection method based on hybrid improved quantum-behavior particle swarm optimization, called HI-BQPSO. The HI-BQPSO combines a filtering method with an improved quantum-behavior particle swarm optimization algorithm to greatly reduce the dimensionality of the data so as to overcome some of the shortcomings of BQPSO. Tests were conducted on nine gene expression datasets and 36 UCI datasets to evaluate and compare the classification accuracy of the HI-BQPSO's selected feature subsets against four other algorithms. The results, using a variety of different classifiers, show that the HI-BQPSO significantly reduces the number of features required for classification while maintaining higher levels of accuracy in many cases.
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