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AIRS
2008
Springer

Efficient Feature Selection in the Presence of Outliers and Noises

8 years 4 months ago
Efficient Feature Selection in the Presence of Outliers and Noises
Although regarded as one of the most successful algorithm to identify predictive features, Relief is quite vulnerable to outliers and noisy features. The recently proposed I-Relief algorithm addresses such deficiencies by using an iterative optimization scheme. Effective as it is, I-Relief is rather time-consuming. This paper presents an efficient alternative that significantly enhances the ability of Relief to handle outliers and strongly redundant noisy features. Our method can achieve comparable performance as I-Relief and has a close-form solution, hence requires much less running time. Results on benchmark information retrieval tasks confirm the effectiveness and efficiency of the proposed method.
Shuang-Hong Yang, Bao-Gang Hu
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where AIRS
Authors Shuang-Hong Yang, Bao-Gang Hu
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