A Study on Relationship Between Generalization Abilities and Fuzziness of Base Classifiers in Ensemble Learning
| Autor: | Hong-Jie Xing, Witold Pedrycz, Xizhao Wang, Yan Li, Qiang Hua, Chun-Ru Dong |
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| Rok vydání: | 2015 |
| Předmět: |
Fuzzy classification
business.industry Applied Mathematics Principle of maximum entropy Linear classifier Pattern recognition Pragmatics Machine learning computer.software_genre Ensemble learning Fuzzy logic Fuzzy classifier Computational Theory and Mathematics Artificial Intelligence Control and Systems Engineering Entropy (information theory) Artificial intelligence business computer Mathematics |
| Zdroj: | IEEE Transactions on Fuzzy Systems. 23:1638-1654 |
| ISSN: | 1941-0034 1063-6706 |
| Popis: | We investigate essential relationships between generalization capabilities and fuzziness of fuzzy classifiers (viz., the classifiers whose outputs are vectors of membership grades of a pattern to the individual classes). The study makes a claim and offers sound evidence behind the observation that higher fuzziness of a fuzzy classifier may imply better generalization aspects of the classifier, especially for classification data exhibiting complex boundaries. This observation is not intuitive with a commonly accepted position in “traditional” pattern recognition. The relationship that obeys the conditional maximum entropy principle is experimentally confirmed. Furthermore, the relationship can be explained by the fact that samples located close to classification boundaries are more difficult to be correctly classified than the samples positioned far from the boundaries. This relationship is expected to provide some guidelines as to the improvement of generalization aspects of fuzzy classifiers. |
| Databáze: | OpenAIRE |
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