A novel algorithm for text classification based on KNN and chaotic binary particle swarm optimization

Abstract

The main problem of chinese text classification is the high dimensional feature space. A novel algorithm for text classification based on KNN and chaotic particle swarm optimization is proposed. The algorithm utilizes chaotic particle swarm algorithm to traverse the feature space of training set and selects feature subspace, then utilizes KNN algorithm to classify text in feature subspace. In the particle swarm's iterative process, chaotic map is used to guide swarms for chaotic search. It makes the algorithm out of local optimum, and expands the ability of finding global optimal solution. Experimental results show that the novel algorithm for chinese text classification is effective, the classification accuracy and recall rate are better than KNN algorithm. © 2013 Springer-Verlag.