Learning Fuzzy Rule Based Classifier with Rule Weights Optimization and Structure Selection by a Genetic Algorithm
This paper presents a method for designing fuzzy rule based systems for pattern recognition. The resulting model is interpretable as linguistic rules and can be used for deep understanding of data. The classifier performance is optimized in the least squares sense and the model complexity is minimiz...
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| creator | Evsukoff, A.G. |
| description | This paper presents a method for designing fuzzy rule based systems for pattern recognition. The resulting model is interpretable as linguistic rules and can be used for deep understanding of data. The classifier performance is optimized in the least squares sense and the model complexity is minimized in a structure selection search, performed by a genetic algorithm The method is tested against benchmark classification problems found in the literature, with good results. |
| doi_str_mv | 10.1109/FUZZY.2007.4295471 |
| format | Conference Proceeding |
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| identifier | ISSN: 1098-7584 |
| ispartof | 2007 IEEE International Fuzzy Systems Conference, 2007, p.1-6 |
| issn | 1098-7584 |
| language | eng |
| recordid | cdi_ieee_primary_4295471 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Association rules Benchmark testing Data mining Design optimization Fuzzy sets Fuzzy systems Genetic algorithms Input variables Machine learning Parameter estimation |
| title | Learning Fuzzy Rule Based Classifier with Rule Weights Optimization and Structure Selection by a Genetic Algorithm |
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