Discriminative Feature Selection by Nonparametric Bayes Error Minimization

	Discriminative Feature Selection by Nonparametric Bayes Error Minimization
	Yang, Shuang-Hong 1; Hu, Bao-Gang2
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
	2012-08-01
卷号	24 期号:8 页码:1422-1434
文章类型	Article
摘要	Feature selection is fundamental to knowledge discovery from massive amount of high-dimensional data. In an effort to establish theoretical justification for feature selection algorithms, this paper presents a theoretically optimal criterion, namely, the discriminative optimal criterion (DoC) for feature selection. Compared with the existing representative optimal criterion (RoC, [21]) which retains maximum information for modeling the relationship between input and output variables, DoC is pragmatically advantageous because it attempts to directly maximize the classification accuracy and naturally reflects the Bayes error in the objective. To make DoC computationally tractable for practical tasks, we propose an algorithmic framework, which selects a subset of features by minimizing the Bayes error rate estimated by a nonparametric estimator. A set of existing algorithms as well as new ones can be derived naturally from this framework. As an example, we show that the Relief algorithm [20] greedily attempts to minimize the Bayes error estimated by the k-Nearest-Neighbor (kNN) method. This new interpretation insightfully reveals the secret behind the family of margin-based feature selection algorithms [28], [14] and also offers a principled way to establish new alternatives for performance improvement. In particular, by exploiting the proposed framework, we establish the Parzen-Relief (P-Relief) algorithm based on Parzen window estimator, and the MAP-Relief (M-Relief) which integrates label distribution into the max-margin objective to effectively handle imbalanced and multiclass data. Experiments on various benchmark data sets demonstrate the effectiveness of the proposed algorithms.
关键词	Feature Selection Discriminative Optimal Criterion Feature Weighting
WOS标题词	Science & Technology ; Technology
关键词[WOS]	BHATTACHARYYA DISTANCE ; ALGORITHMS
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000305900500006
引用统计	被引频次：24[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/2845
专题	多模态人工智能系统全国重点实验室_多媒体计算
作者单位	1.Georgia Inst Technol, Coll Comp, Atlanta, GA 30318 USA 2.Chinese Acad Sci, NLPR, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Shuang-Hong,Hu, Bao-Gang. Discriminative Feature Selection by Nonparametric Bayes Error Minimization[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2012,24(8):1422-1434.
APA	Yang, Shuang-Hong,&Hu, Bao-Gang.(2012).Discriminative Feature Selection by Nonparametric Bayes Error Minimization.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,24(8),1422-1434.
MLA	Yang, Shuang-Hong,et al."Discriminative Feature Selection by Nonparametric Bayes Error Minimization".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 24.8(2012):1422-1434.

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