Particle swarm optimization based semi-supervised learning on Chinese text categorization
Shi Cheng, Yuhui Shi, Quande Qin · 2012
For many large scale learning problems, acquiring a large amount of labeled training data is expensive and time-consuming. Semi-supervised learning is a machine learning paradigm which deals with utilizing unlabeled data to build better classifiers. However, unlabeled data with wrong predictions will mislead the classifier. In this paper, we proposed a particle swarm optimization based semi-learning classifier to solve Chinese text categorization problem. This classifier utilizes an iterative strategy, and the result of classifier is determined by a document's previous prediction and its neighbors' information. The new classifier is tested on a Chinese text corpus. The proposed classifier is compared with the k nearest neighbor method, the k weighted nearest neighbor method, and the self-learning classifier.