Impact of fuzziness categorization on divide and conquer strategy for instance selection

Fuzziness based divide and conquer (D&C) is a recently proposed strategy for promoting the classifiers (i.e., fuzzy classifiers) performance, where the amount of fuzziness quantity associated with each data point (i.e., both labeled and unlabeled) is considered as an important avenue to the empire for instance selection problem. This technique is regarded as a semi-supervised learning (SSL) technique, where different categories of instances are obtained by using fuzziness measure, and then the instances having less amount of fuzziness are incorporated into training set for improving the generalization ability of a classifier. This study proposes some effective methods and presents a novel algorithm for categorizing the instances into three groups that can effectively integrate with D&C strategy. It is observed by the experimental validation that considering the splitting criteria for instances categorization can lead the classifier to perform better on withheld set. Results on different classification data sets prove the effectiveness of proposed algorithm.