Feature Selection for Classification through Population Random Search with Memory

We propose a new approach for feature selection. The proposed approach is based on a combination of random and heuristic search strategies. The solution is represented as a binary vector whose dimension is determined by the number of features in the dataset. New solutions are generated at random using a normal and uniform distribution. The heuristic underlying the proposed approach can be formulated as follows: the chance of a feature to get into the next generation is proportional to the frequency of this feature appearing in previous best solutions. The proposed approach has been tested on several datasets from the KEEL repository. We also show an experimental comparison with other methods.