Conference proceeding
A New Rough Set Model for Knowledge Acquisition in Incomplete Information System
2009 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING ( GRC 2009), pp 696-701
01 Jan 2009
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Rough set models based on the tolerance and similarity relations, are constructed to deal with incomplete information systems. Unfortunately, tolerance and similarity relations have their own limitations because the former is too loose while the latter is too strict in classification analysis. To make a reasonable and flexible classification in incomplete information system, a new binary relation is proposed in this paper. This new binary relation is only reflective and it is a generalization of tolerance and similarity relations. Furthermore, three different rough set models based on the above three different binary relations are compared and then some important properties are obtained. Finally, the direct approach to certain and possible rules induction in incomplete information system is investigated, an illustrative example is analyzed to substantiate the conceptual arguments.
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Details
- Title
- A New Rough Set Model for Knowledge Acquisition in Incomplete Information System
- Creators
- Xibei Yang - Nanjing University of Science and TechnologyJingyu Yang - Nanjing University of Science and TechnologyXiaohua Hu - Drexel University
- Contributors
- T Y Lin (Editor)X H Hu (Editor) - Drexel UniversityJ L Xia (Editor)T P Hong (Editor)Z Z Shi (Editor)J C Han (Editor)S Tsumoto (Editor)Z J Shen (Editor)
- Publication Details
- 2009 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING ( GRC 2009), pp 696-701
- Conference
- 2009 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING ( GRC 2009)
- Publisher
- IEEE
- Number of pages
- 2
- Grant note
- 60472060; 60572034; 60632050 / Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000287830500146
- Scopus ID
- 2-s2.0-70449984986
- Other Identifier
- 991019167674304721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic