Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 23, Issue 5, Pages 1638-1654Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2014.2371479
Keywords
Classification; decision boundary; fuzziness; fuzzy classifier; generalization
Funding
- National Natural Science Fund of China [61170040, 71371063]
- Hebei NSF [F2013201110, F2013201060, F2014201100, ZD2010139]
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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.
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