Feature Reduction with Inconsistency

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Author(s): Yong Liu (Institute of Cyber-Systems and Control of Zhejiang University, China), Yunliang Jiang (Huzhou Teachers College, China)and Jianhua Yang (SCI-Tech Academy of Zhejiang University, China)
Copyright: 2012
Pages: 10
Source title: Developments in Natural Intelligence Research and Knowledge Engineering: Advancing Applications
Source Author(s)/Editor(s): Yingxu Wang (University of Calgary, Canada)
DOI: 10.4018/978-1-4666-1743-8.ch014

Keywords: Cognitive Informatics / Computer Science & IT / Engineering Science Reference

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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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