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PROJECT TITLE :

A Study on Relationship Between Generalization Abilities and Fuzziness of Base Classifiers in Ensemble Learning

ABSTRACT:

We tend to 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 proof behind the observation that higher fuzziness of a fuzzy classifier may imply higher generalization aspects of the classifier, particularly for classification data exhibiting advanced boundaries. This observation is not intuitive with a commonly accepted position in “ancient” pattern recognition. The link that obeys the conditional maximum entropy principle is experimentally confirmed. Furthermore, the link can be explained by the very fact that samples located shut to classification boundaries are additional troublesome to be properly classified than the samples positioned way from the boundaries. This relationship is anticipated to produce some guidelines on the improvement of generalization aspects of fuzzy classifiers.


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