Evaluation of Feature Combination Approaches for Text Categorisation

Text categorisation relies heavily on feature selection. Both the possible reduction in dimensionality as well as improvements in classification performance are highly desirable. To the end of feature selection for text, a range of different methods have been developed, each having unique properties and selecting different features. However, it remains unclear which of them can be combined and what benefits this brings with it. In this paper we present correlation methods for the analysis of feature rankings and evaluate the combination of features according to these metrics. We further show results of an extensive study of feature selection approaches using a wide range of combination methods. We performed experiments on 19 test collections and report our findings.

• Type: Journal
• Year: 2011
• Where: ISMIS
• Authors: Robert Neumayer, Kjetil Nørvåg
• Keywords: Artificial Intelligence, Combination Methods, Correlation Methods, ISMIS 2011, Test Collections