A New Algorithm for Discretization in Rough Sets Based on Particle Swarm Optimization

摘要

Rough set theory is a new valid mathematical tool to solve many real-life problems in machine learning. But it can only deal with the discrete attributes. Many discretization algorithms have been used at present, but there is not the complete criterion of the best discretization, it is difficult to get more satisfactory result for most algorithms. A new method for discretization based on Particle Swarm Optimization (PSO) is presented in this paper. We look upon the position of one kind demarcation points as a particle to search for its best position on the premise of keeping the primary partition capability in the discrete decision table, and there is little conflictive data. The experimental results prove the validity of this method.