Adaptive fuzzy rule-based classification system integrating both expert knowledge and data
This paper presents an adaptive fuzzy rule-based classification system using a new hybrid modeling method that integrates both expert knowledge and new knowledge learnt from data. Inspired by human learning, the membership functions of fuzzy rules are optimized based on a hybrid error function that...
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/99301 http://hdl.handle.net/10220/12873 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This paper presents an adaptive fuzzy rule-based classification system using a new hybrid modeling method that integrates both expert knowledge and new knowledge learnt from data. Inspired by human learning, the membership functions of fuzzy rules are optimized based on a hybrid error function that combines errors caused by the class predefined by expert knowledge and nearby historical data. The weights of the two errors can be adjusted by a conservative parameter. Experimental results show that our method significantly reduces classification ambiguity in 9 datasets. |
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