Title
FSMJ: Feature selection with maximum Jensen-Shannon divergence for text categorization
Document Type
Conference Proceeding
Date of Original Version
9-27-2016
Abstract
In this paper, we present a new wrapper feature selection approach based on Jensen-Shannon (JS) divergence, termed feature selection with maximum JS-divergence (FSMJ), for text categorization. Unlike most existing feature selection approaches, the proposed FSMJ approach is based on real-valued features which provide more information for discrimination than binary-valued features used in conventional approaches. We show that the FSMJ is a greedy approach and the JS-divergence monotonically increases when more features are selected. We conduct several experiments on real-life data sets, compared with the state-of-the-art feature selection approaches for text categorization. The superior performance of the proposed FSMJ approach demonstrates its effectiveness and further indicates its wide potential applications on data mining.
Publication Title, e.g., Journal
Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Volume
2016-September
Citation/Publisher Attribution
Tang, Bo, and Haibo He. "FSMJ: Feature selection with maximum Jensen-Shannon divergence for text categorization." Proceedings of the World Congress on Intelligent Control and Automation (WCICA) 2016-September, (2016): 3143-3148. doi: 10.1109/WCICA.2016.7578786.