Research on Classification Effectiveness of the Novel Mamdani Fuzzy Classifier
| Autor: | Hong Yan Zuo, Zhou Quan Luo, Chao Wu |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Fitness function
Fuzzy classification Neuro-fuzzy business.industry Pattern recognition General Medicine Fuzzy control system Machine learning computer.software_genre Defuzzification Set (abstract data type) ComputingMethodologies_PATTERNRECOGNITION Outlier Fuzzy set operations Artificial intelligence business computer Mathematics |
| Zdroj: | Applied Mechanics and Materials. :871-874 |
| ISSN: | 1662-7482 |
| DOI: | 10.4028/www.scientific.net/amm.511-512.871 |
| Popis: | A novel Mamdani fuzzy classifier based on improved chaos immune algorithm is developed, in which bilateral Gaussian membership function parameters are set as constraint conditions and the indexes of fuzzy classification effectiveness and number of correct samples of fuzzy classification as the subgoal of fitness function. Moreover, Iris database is used for classification effectiveness simulation experiment. The results show that Mamdani fuzzy classifier based on improved chaos immune algorithm can effectively improve the prediction accuracy of classification of data sets with noises and outliers. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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