| Feature subset selection based on mahalanobis distance： a statistical rough set method |
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| 引用本文： | 孙亮,韩崇昭.Feature subset selection based on mahalanobis distance： a statistical rough set method[J].西安交通大学学报(英文版),2008,20(1):14-18. |
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| 作者姓名： | 孙亮 韩崇昭 |
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| 作者单位： | [1]School of Electronic and Information Engineering, Xi＇an Jiaotong University, Xi＇an 710049 [2]Information Engineering University, Zhengzhou 4500001, China. |
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| 基金项目： | 国家重点基础研究发展计划(973计划) |
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| 摘 要： |
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| 关 键 词： | 特征子集选择 粗糙集 特性试验 马哈拉诺比斯距离 |
| 文章编号： | 1671-8267（2008）01-0014-05 |
Feature subset selection based on mahalanobis distance: a statistical rough set method |
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| Sun Liang,Han Chongzhao.Feature subset selection based on mahalanobis distance: a statistical rough set method[J].Academic Journal of Xi’an Jiaotong University,2008,20(1):14-18. |
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| Authors: | Sun Liang Han Chongzhao |
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| Abstract: | In order to select effective feature subsets for pattern classification, a novel statistics rough set method is presented based on generalized attribute reduction. Unlike classical reduction approaches, the objects in universe of discourse are signs of training sample sets and values of attributes are taken as statistical parameters. The binary relation and discernibility matrix for the reduction are induced by distance function. Furthermore, based on the monotony of the distance function defined by Mahalanobis distance, the effective feature subsets are obtained as generalized attribute reducts. Experiment result shows that the classification performance can be improved by using the selected feature subsets. |
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| Keywords: | feature subset selection rough set attribute reduction Mahalanobis distance |
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