Hybrid Feature Selection and Weighting Method Based on Binary Particle Swarm Optimization

This work proposes an optimization technique based on binary particle swarm optimization that performs feature selection and feature weighting simultaneously. In the optimization process, each member of the population is described as a vector having three parts: i) one weight per feature (feature weighting), ii) one binary value per feature indicating the presence or the absence of the feature (feature selection), and, iii) the number of neighbors of the kNN classifier. After optimization, this vector is used as a mask to generate a new subset of features that is evaluated using the kNN classifier. The experimental study was performed on public datasets and showed that the proposed technique obtains better accuracy and reduction rates than state-of-the-art techniques.