Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/58212
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Type: Conference paper
Title: Rank aggregation based text feature selection
Author: Wu, O.
Zuo, H.
Zhu, M.
Hu, W.
Gao, J.
Wang, H.
Citation: Proceedings of the 2009 IEEE/WIC/ACM, 2009: pp.165-172
Publisher: IEEE
Publisher Place: USA
Issue Date: 2009
ISBN: 9780769538013
Conference Name: IEEE/WIC/ACM International Conference on Intelligent Agent Technology (2009 : Italy)
Statement of
Responsibility: 
Ou Wu, Haiqiang Zuo, Mingliang Zhu, Weiming Hu, Jun Gao and Hanzi Wang
Abstract: Filtering feature selection method (filtering method, for short) is a well-known feature selection strategy in pattern recognition and data mining. Filtering method outperforms other feature selection methods in many cases when the dimension of features is large. There are so many filtering methods proposed in previous work leading to the “selection trouble” that how to select an appropriate filtering method for a given text data set. Since to find the best filtering method is usually intractable in real application, this paper takes an alternative path. We propose a feature selection framework that fuses the results obtained by different filtering methods. In fact, deriving a better rank list from different rank lists, known as rank aggregation, is a hot topic studied in many disciplines. Based on the proposed framework and Markov chains rank aggregation techniques, in this paper, we present two new feature selection methods: FR-MC1 and FR-MC4. We also introduce a perturbation algorithm to alleviate the drawbacks of Markov chains rank aggregation techniques. Empirical evaluation on two public text data sets shows that the two new feature selection methods achieve better or comparable results than classical filtering methods, which also demonstrate the effectiveness of our framework.
Rights: © 2009 IEEE
RMID: 0020096480
DOI: 10.1109/WI-IAT.2009.32
Appears in Collections:Computer Science publications

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