Affiliations: [a] College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China | [b] College of Science, China Agricultural University, Beijing, China
Correspondence:
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Corresponding author. Xi-Zhao Wang, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China. Tel./Fax: +86 13700326811; E-mail: [email protected]
Abstract: This paper investigates a relationship between the fuzziness of a classifier and the misclassification rate of the classifier on a group of samples. For a given trained classifier that outputs a membership vector, we demonstrate experimentally that samples with higher fuzziness outputted by the classifier mean a bigger risk of misclassification. We then propose a fuzziness category based divide-and-conquer strategy which separates the high-fuzziness samples from the low fuzziness samples. A particular technique is used to handle the high-fuzziness samples for promoting the classifier performance. The reasonability of the approach is theoretically explained and its effectiveness is experimentally demonstrated.
Keywords: Fuzziness, misclassification, generalization, boundary point, Divide-and-Conquer strategy
