Efficient Feature Selection in the Presence of Outliers and Noises

摘要：

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 eff-iciency of the proposed method.

作者: Shuang-Hong Yang Bao-Gang Hu

作者单位: National Lab of Pattern Recognition (NLPR) & Sino-French IT Lab(LlAMA) Institute of Automation,Chinese Academy of Sciences

会议类型: 国际会议

会议名称: 4th Asia Information Retrieval Symposium(AIRS 2008)(第四届亚洲信息检索研讨会)

会议地点: 哈尔滨

会议语种:英文

页码: 184-191

在线出版日期: 2008-01-16（万方平台首次上网日期，不代表论文的发表时间）