A Multi-objective Particle Swarm Optimization Algorithm for Rule Discovery


Rule discovery is usually posed as a multi-objective optimization problem with two criteria, predictive accuracy and comprehensibility. Single-objective particle swarm optimization algorithm, which combines the two criteria into one, has been shown to have convincing results on the classification tasks. However, it does not take the nature of the optimality conditions for multiple objectives into account. It is well known that accuracy and comprehensibility are hardly attainable simultaneously, which makes the optimization problem difficult to solve efficiently. In this paper, we propose a multi-objective PSO algorithm to solve the problem. The experimental result shows that our algorithm has better performance than its single-objective counterpart.