Multi-view collaborative semi-supervised classification algorithm based on diversity measurers of classifier with the combination of agreement and disagreement label rules

Considering the deficiencies of the Co-training algorithm including redundancy and sufficient conditions, diversity measurers of classifier, combined label rules, unoptimizable classifier during model updating process etc., a multi-view collaborative semi-supervised classification algorithm based on diversity measurers of the classifier with the combination of agreement and disagreement label rules (Co-AgDiag) is proposed. This algorithm uses a combination of agreement and disagreement label rules during the labelling unlabelled data process by judging whether the two classifiers are consistent and considering the diversity threshold in order to improve the performance of the classifier. During the process of generating classifier and model updating, we employed the diversity rule of classifier to update the model in order to judge the performance of the two classifiers. The experimental results on UCI datasets demonstrate the effectively and feasibility of the proposed algorithm for multi-view classification problems.

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