Evaluation and analysis of bio-inspired optimization algorithms for feature selection

Wrapper-based feature (subset) selection is widely used as an effective means for decreasing the dimensionality of datasets. However, it is not the most efficient approach in terms of computational cost. Hence, the choice of the wrapper is paramount. Ideally, the wrapper should be simple to use and understand, whilst yielding good solutions as fast as possible. Bioinspired optimisation algorithms are a common choice in that regard, but not all are made equally. This paper investigates a number of optimisers on diverse datasets in order to provide an insight into their efficiency and behaviour with respect to the problem of dimensionality reduction for classification needs. Correspondingly, some guidelines concerning the choice of the wrapper are given.