δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions
Decision-theoretic rough set is a quite useful rough set by introducing the decision cost into probabilistic approximations of the target. However, Yao’s decision-theoretic rough set is based on the classical indiscernibility relation; such a relation may be too strict in many applications. To solve...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/382439 |
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| author | Hengrong Ju Huili Dou Yong Qi Hualong Yu Dongjun Yu Jingyu Yang |
| author_facet | Hengrong Ju Huili Dou Yong Qi Hualong Yu Dongjun Yu Jingyu Yang |
| author_sort | Hengrong Ju |
| collection | DOAJ |
| description | Decision-theoretic rough set is a quite useful rough set by introducing the decision cost into probabilistic approximations of the target. However, Yao’s decision-theoretic rough set is based on the classical indiscernibility relation; such a relation may be too strict in many applications. To solve this problem, a δ-cut decision-theoretic rough set is proposed, which is based on the δ-cut quantitative indiscernibility relation. Furthermore, with respect to criterions of decision-monotonicity and cost decreasing, two different algorithms are designed to compute reducts, respectively. The comparisons between these two algorithms show us the following: (1) with respect to the original data set, the reducts based on decision-monotonicity criterion can generate more rules supported by the lower approximation region and less rules supported by the boundary region, and it follows that the uncertainty which comes from boundary region can be decreased; (2) with respect to the reducts based on decision-monotonicity criterion, the reducts based on cost minimum criterion can obtain the lowest decision costs and the largest approximation qualities. This study suggests potential application areas and new research trends concerning rough set theory. |
| format | Article |
| id | doaj-art-0424945a70d945899aafe050f52c4581 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-0424945a70d945899aafe050f52c45812025-02-03T06:01:34ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/382439382439δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute ReductionsHengrong Ju0Huili Dou1Yong Qi2Hualong Yu3Dongjun Yu4Jingyu Yang5School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, ChinaSchool of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, ChinaSchool of Economics and Management, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, ChinaSchool of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210093, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210093, ChinaDecision-theoretic rough set is a quite useful rough set by introducing the decision cost into probabilistic approximations of the target. However, Yao’s decision-theoretic rough set is based on the classical indiscernibility relation; such a relation may be too strict in many applications. To solve this problem, a δ-cut decision-theoretic rough set is proposed, which is based on the δ-cut quantitative indiscernibility relation. Furthermore, with respect to criterions of decision-monotonicity and cost decreasing, two different algorithms are designed to compute reducts, respectively. The comparisons between these two algorithms show us the following: (1) with respect to the original data set, the reducts based on decision-monotonicity criterion can generate more rules supported by the lower approximation region and less rules supported by the boundary region, and it follows that the uncertainty which comes from boundary region can be decreased; (2) with respect to the reducts based on decision-monotonicity criterion, the reducts based on cost minimum criterion can obtain the lowest decision costs and the largest approximation qualities. This study suggests potential application areas and new research trends concerning rough set theory.http://dx.doi.org/10.1155/2014/382439 |
| spellingShingle | Hengrong Ju Huili Dou Yong Qi Hualong Yu Dongjun Yu Jingyu Yang δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions The Scientific World Journal |
| title | δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions |
| title_full | δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions |
| title_fullStr | δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions |
| title_full_unstemmed | δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions |
| title_short | δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions |
| title_sort | δ cut decision theoretic rough set approach model and attribute reductions |
| url | http://dx.doi.org/10.1155/2014/382439 |
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