Particle swarm optimization based semi-supervised learning on Chinese text categorization

Shi Cheng*, Yuhui Shi, Quande Qin

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

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Abstract

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.

Original languageEnglish
Title of host publication2012 IEEE Congress on Evolutionary Computation, CEC 2012
DOIs
Publication statusPublished - 2012
Event2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Publication series

Name2012 IEEE Congress on Evolutionary Computation, CEC 2012

Conference

Conference2012 IEEE Congress on Evolutionary Computation, CEC 2012
Country/TerritoryAustralia
CityBrisbane, QLD
Period10/06/1215/06/12

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Cheng, S., Shi, Y., & Qin, Q. (2012). Particle swarm optimization based semi-supervised learning on Chinese text categorization. In 2012 IEEE Congress on Evolutionary Computation, CEC 2012 Article 6252959 (2012 IEEE Congress on Evolutionary Computation, CEC 2012). https://doi.org/10.1109/CEC.2012.6252959