Rank-score characteristics (RSC) function and cognitive diversity
BI'10 Proceedings of the 2010 international conference on Brain informatics
AMT'11 Proceedings of the 7th international conference on Active media technology
BI'11 Proceedings of the 2011 international conference on Brain informatics
Computer Methods and Programs in Biomedicine
BizPro: Extracting and categorizing business intelligence factors from textual news articles
International Journal of Information Management: The Journal for Information Professionals
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Feature selection is an important method for improving the efficiency and accuracy of text categorization algorithmsby removing redundant and irrelevant terms from the corpus.Extensive researches have been done to improve the performance ofindividual feature selection methods, but not much on their combinations.In this paper, we propose a method of combining multiple feature selection methods by using the Combinatorial Fusion Analysis (CFA). A rank-score function and its graph, called rank-score graph,are adopted to measure the diversity of different feature selection methods.We have shown that a combination of multiple feature selection methods can outperform a single method only if each individual feature selection method has unique scoring behavior and relatively high performance. Moreover, it is shown that the rank-score function and rank-score graph are useful for the selection of a combination of feature selection methods.