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Feature Reduction with Inconsistency

Feature Reduction with Inconsistency

Yong Liu, Yunliang Jiang, Jianhua Yang
Copyright: © 2012 |Pages: 10
ISBN13: 9781466617438|ISBN10: 1466617438|EISBN13: 9781466617445
DOI: 10.4018/978-1-4666-1743-8.ch014
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MLA

Liu, Yong, et al. "Feature Reduction with Inconsistency." Developments in Natural Intelligence Research and Knowledge Engineering: Advancing Applications, edited by Yingxu Wang, IGI Global, 2012, pp. 195-204. https://doi.org/10.4018/978-1-4666-1743-8.ch014

APA

Liu, Y., Jiang, Y., & Yang, J. (2012). Feature Reduction with Inconsistency. In Y. Wang (Ed.), Developments in Natural Intelligence Research and Knowledge Engineering: Advancing Applications (pp. 195-204). IGI Global. https://doi.org/10.4018/978-1-4666-1743-8.ch014

Chicago

Liu, Yong, Yunliang Jiang, and Jianhua Yang. "Feature Reduction with Inconsistency." In Developments in Natural Intelligence Research and Knowledge Engineering: Advancing Applications, edited by Yingxu Wang, 195-204. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-1743-8.ch014

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Abstract

Feature selection is a classical problem in machine learning, and how to design a method to select the features that can contain all the internal semantic correlation of the original feature set is a challenge. The authors present a general approach to select features via rough set based reduction, which can keep the selected features with the same semantic correlation as the original feature set. A new concept named inconsistency is proposed, which can be used to calculate the positive region easily and quickly with only linear temporal complexity. Some properties of inconsistency are also given, such as the monotonicity of inconsistency and so forth. The authors also propose three inconsistency based attribute reduction generation algorithms with different search policies. Finally, a “mini-saturation” bias is presented to choose the proper reduction for further predictive designing.

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