Feature Reduction with Inconsistency

Authors:
Yong Liu;Yunliang Jiang;Jianhua Yang
Affiliations:
Institute of Cyber-Systems and Control of Zhejiang University, China;Huzhou Teachers College, China;SCI-Tech Academy of Zhejiang University, China
Venue:
International Journal of Cognitive Informatics and Natural Intelligence
Year:
2010

Citing 7
Cited 2

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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.