Robust Gaussian kernel based approach for feature selection

被引：4
|
作者：
Hsiao, Chih-Ching [1 ]
Chuang, Chen-Chia [2 ]
Su, Shun-Feng [3 ]
机构：
[1] Department of Information Technology, Kao Yuan University, Taiwan
[2] Department of Electrical Engineering, National Ilan University, Taiwan
[3] Department of Electrical Engineering, National Taiwan University of Science and Technology, Taiwan
来源：
Advances in Intelligent Systems and Computing | 2014年 / 268卷
关键词：
Rough set theory - Statistics;
D O I：
10.1007/978-3-319-05500-8_4
中图分类号：
学科分类号：
摘要：
The outlier problem of feature selection is rarely discussed in the most previous works. Moreover, there are no work has been reported in literature on symbolic interval feature selection in the supervised framework. In this paper, we will incorporate similarity margin concept and Gaussian kernel fuzzy rough sets to deal with the Symbolic Data Selection problem and it is also an optimizing problem. The advantage of this approach is it can easily introduce loss function and with robustness. © Springer International Publishing Switzerland 2014.
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页码：25 / 33
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