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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jian Lirong Liu Sifeng |
| Copyright Year | 2009 |
| Description | Author affiliation: College of Economic & Management, Nanjing University of Aeronautics and Astronautics, 210016, P.R. China (Jian Lirong; Liu Sifeng) |
| Abstract | The paper proposes a hybrid approach of grey rough set and probabilistic neural network for uncertain decision. Grey rough set model is tolerant of noise. By setting a level of grey degree, redundant attributes are eliminated from decision table, a minimal knowledge representation is derived and the set of rules are generated through the grey rough set model. Subsequently, the reduced decision table is forwarded to probabilistic neural networks for classification and decision. The additional properties to PNN provided by the grey rough set analysis are input dimensionality reduction by the elimination of irrelevant features, a fast learning process, explanation facilities providing, hidden patterns finding in data and uncertainty treatment. The research result reveals that the hybrid approach has a high accuracy in classification and decision. The method can be applied to uncertain decision with ambiguous, incomplete and noisy database. |
| Starting Page | 1101 |
| Ending Page | 1106 |
| File Size | 600302 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424449149 |
| DOI | 10.1109/GSIS.2009.5408075 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-11-10 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Intelligent networks Uncertainty Statistical analysis Databases Neural networks Machine learning Set theory Pattern analysis Expert systems Hybrid intelligent systems |
| Content Type | Text |
| Resource Type | Article |
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