Particle swarm optimization based nearest neighbor algorithm on Chinese text categorization

Abstract

In this paper, the nearest neighbor method on Chinese text categorization is formulated as an optimization problem. The particle swarm optimization is utilized to optimize a nearest neighbor classifier to solve the Chinese text categorization problem. The parameter k was first optimized to obtain the minimum error, then the categorization problem is formulated as a discrete, constrained, and single objective optimization problem. Each dimension of solution vector is dependent on each other in the solution space. The parameter k and the number of labeled examples for each class are optimized together to reach the minimum categorization error. In the experiment, with the utilization of particle swarm optimization, the performance of a nearest neighbor algorithm can be improved, and the algorithm can obtain the minimum categorization error rate.