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Efficient feature selection for high-dimensional data using two-level filter

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4 Author(s)
Yun Li ; Dept. of Comput., Chongqing Univ., China ; Zhong-Fu Wu ; Jia-Min Liu ; Yan-Yun Tang

Feature selection is a key problem to pattern recognition and machine learning, and it is difficult to get the optimal feature subset for its NP-hard. Currently, the dimensionality of feature set or instance set is very high in many applications, such as information retrieval, so the feature selection from high-dimensional data is also an urgent task for researchers. This paper presents a new approach, which is a two-level filter model system integrating the relief and a newly developed algorithm of feature cluster, to reduce the dimensionality of large-scale feature set via the feature correlation (relevance) including the feature-feature correlation and feature-class correlation. Our major contributions are: (1) to present a system to perform feature selection from high-dimensional data; (2) to analyze the change of system architecture according to the time cost of the parts in the system; (3) to summarize and comment on the calculations of feature correlation; (4) to perform experiments to show the effective of the proposed approach, which has shown that the system can efficiently get a better compromise between dimensionality reduction and accuracy rate of classification than just part of the system. In many cases, it can improve the accuracy rate and dimensionality reduction.

Published in:

Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on  (Volume:3 )

Date of Conference:

26-29 Aug. 2004