Evolved feature weighting for random subspace classifier

The problem addressed in this paper concerns the multi-classifier generation by a Random Subspace method. In the random subspace method the classifiers are constructed in random subspaces of the data feature space. In this work we propose an evolved feature weighting approach: in each subspace the features are multiplied by a weight factor for minimizing the error rate in the training set. An efficient method based on Particle Swarm Optimization is here proposed for finding a set of weights for each feature in each subspace. The performance improvement with respect to the state-of-the-art approaches is validated through experiments with several benchmark datasets.