Examples initialization in Chinese text categorization

The generalization ability is a fundamental goal for a classifier in machine learning. The categorization results are influenced by the initialized examples in a nearest neighbor classifier. The generalization ability beyond the examples in training set is important in categorization. In this paper, we propose a particle swarm optimization with k means clustering algorithm for the nearest neighbor classifier´s examples initialization to improve categorization performances. This classifier utilizes an iterative strategy, and the classifier´s example initialization is based on clusters center and random examples. The new classifier is tested on a Chinese text corpus. The proposed classifier is compared against the nearest neighbor classifier with random initialization.