Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29750
Title: Feature selection for monotonic classification
Authors: Hu, Q
Pan, W
Zhang, L 
Zhang, D 
Song, Y
Guo, M
Yu, D
Keywords: Feature selection
Fuzzy ordinal set
Monotonic classification
Rank mutual information (RMI)
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on fuzzy systems, 2012, v. 20, no. 1, 6011677, p. 69-81 How to cite?
Journal: IEEE transactions on fuzzy systems 
Abstract: Monotonic classification is a kind of special task in machine learning and pattern recognition. Monotonicity constraints between features and decision should be taken into account in these tasks. However, most existing techniques are not able to discover and represent the ordinal structures in monotonic datasets. Thus, they are inapplicable to monotonic classification. Feature selection has been proven effective in improving classification performance and avoiding overfitting. To the best of our knowledge, no technique has been specially designed to select features in monotonic classification until now. In this paper, we introduce a function, which is called rank mutual information, to evaluate monotonic consistency between features and decision in monotonic tasks. This function combines the advantages of dominance rough sets in reflecting ordinal structures and mutual information in terms of robustness. Then, rank mutual information is integrated with the search strategy of min-redundancy and max-relevance to compute optimal subsets of features. A collection of numerical experiments are given to show the effectiveness of the proposed technique.
URI: http://hdl.handle.net/10397/29750
ISSN: 1063-6706
EISSN: 1941-0034
DOI: 10.1109/TFUZZ.2011.2167235
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