Optimal feature selection algorithm based on quantum-inspired clone genetic strategy in text categorization

Authors:
Hao Chen;Beiji Zou
Affiliations:
Central South University, Changsha, China;Central South University, Changsha, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

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Abstract

Information overload is a serious issue in the modern society. As a powerful method to help people out of being "lost" in too much useless information, automatic text categorization is getting more and more important. Feature selection is the most important step in text categorization. To improve the performance of text categorization, we present a new text categorization method called quantum-inspired clone genetic algorithm (QCGA). The experimental results show that the QCGA algorithm is superior to other common methods.