Knowledge granularity based incremental attribute reduction for incomplete decision systems
Attribute reduction is an important application of rough set theory. With the dynamic changes of data becoming more and more common, traditional attribute reduction, also called static attribute reduction, is no longer efficient. How to update attribute reducts efficiently gets more and more attenti...
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| Veröffentlicht in: | International journal of machine learning and cybernetics 2020-05, Vol.11 (5), p.1141-1157 |
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| container_title | International journal of machine learning and cybernetics |
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| creator | Zhang, Chucai Dai, Jianhua Chen, Jiaolong |
| description | Attribute reduction is an important application of rough set theory. With the dynamic changes of data becoming more and more common, traditional attribute reduction, also called static attribute reduction, is no longer efficient. How to update attribute reducts efficiently gets more and more attention. In the light of the variation about the number of objects, we focus on incremental attribute reduction approaches based on knowledge granularity which can be used to measure the uncertainty in incomplete decision systems. We first introduce incremental mechanisms to calculate knowledge granularity for incomplete decision systems when multiple objects vary dynamically. Then, incremental attribute reduction algorithms for incomplete decision systems when adding multiple objects and when deleting multiple objects are proposed respectively. Finally, comparative experiments on different real-life data sets are conducted to demonstrate the effectiveness and efficiency of the proposed incremental algorithms for updating attribute reducts with the variation of multiple objects in incomplete decision systems. |
| doi_str_mv | 10.1007/s13042-020-01089-4 |
| format | Article |
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J. Mach. Learn. & Cyber</addtitle><description>Attribute reduction is an important application of rough set theory. With the dynamic changes of data becoming more and more common, traditional attribute reduction, also called static attribute reduction, is no longer efficient. How to update attribute reducts efficiently gets more and more attention. In the light of the variation about the number of objects, we focus on incremental attribute reduction approaches based on knowledge granularity which can be used to measure the uncertainty in incomplete decision systems. We first introduce incremental mechanisms to calculate knowledge granularity for incomplete decision systems when multiple objects vary dynamically. Then, incremental attribute reduction algorithms for incomplete decision systems when adding multiple objects and when deleting multiple objects are proposed respectively. 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| subjects | Algorithms Approximation Artificial Intelligence Complex Systems Computational Intelligence Control Datasets Decision theory Engineering Entropy Feature selection Group dynamics Information systems Knowledge Mechatronics Original Article Pattern Recognition Reduction Robotics Set theory Systems Biology |
| title | Knowledge granularity based incremental attribute reduction for incomplete decision systems |
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