| Heuristic Reduction Algorithm Based on Pairwise Positive Region |
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| 作者姓名： | 祁立 刘玉树 |
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| 作者单位： | School of Computer Science and Technology Beijing Institute of Technology，School of Computer Science and Technology，Beijing Institute of Technology，Beijing 100081，China，Beijing 100081，China |
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| 基金项目： | Sponsored by the Ministerial Level Advanced Research Foundation(11415133) |
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| 摘 要： | To guarantee the optimal reduct set, a heuristic reduction algorithm is proposed, which considers the distinguishing information between the members of each pair decision classes. Firstly the pairwise positive region is defined, based on which the pairwise significance measure is calculated between the members of each pair classes. Finally the weighted pairwise significance of attribute is used as the attribute reduction criterion, which indicates the necessity of attributes very well. By introducing the noise tolerance factor, the new algorithm can tolerate noise to some extent. Experimental results show the advantages of our novel heuristic reduction algorithm over the traditional attribute dependency based algorithm.
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| 关 键 词： | 粗糙集合 人工智能 遗传算法 计算机 |
| 文章编号： | 1004-0579（2007）03-0295-05 |
| 收稿时间： | 2006/9/26 0:00:00 |
| 修稿时间： | 2006-09-15 |
Heuristic Reduction Algorithm Based on Pairwise Positive Region |
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| QI Li and LIU Yu-shu.Heuristic Reduction Algorithm Based on Pairwise Positive Region[J].Journal of Beijing Institute of Technology,2007,16(3):295-299. |
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| Authors: | QI Li and LIU Yu-shu |
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| Affiliation: | School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China |
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| Abstract: | To guarantee the optimal reduct set, a heuristic reduction algorithm is proposed, which considers the distinguishing information between the members of each pair decision classes. Firstly the pairwise positive region is defined, based on which the pairwise significance measure is calculated between the members of each pair classes. Finally the weighted pairwise significance of attribute is used as the attribute reduction criterion, which indicates the necessity of attributes very well. By introducing the noise tolerance factor, the new algorithm can tolerate noise to some extent. Experimental results show the advantages of our novel heuristic reduction algorithm over the traditional attribute dependency based algorithm. |
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| Keywords: | rough set pairwise positive region heuristic reduction algorithm |
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