Particle Swarm Classification for High Dimensional Data Sets

This work studies the use of Particle Swarm Optimization (PSO) as a classification technique. Beyond assessing classification accuracy, it investigates the following questions: does PSO present limitations for high dimensional application domains? Is it less efficient for multi class problems? To answer the questions, an experimental set up was realized that uses three high dimensional data sets. Our results are that, depending on the mechanisms controlling confinement and dispersion in the PSO algorithm, the classification accuracy varied with the dimensionality of the data and the cardinality of the output space.