Rough Co-training Model for Incomplete Weakly Labeled Data
GAO Can1,2, ZHOU Jie1,2, GAO Tianyu3, LAI Zhihui1,2
1.College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060 2.Faculty of Applied Science and Textiles, Hong Kong Polytechnic University, Hong Kong 3.School of Minerals Processing and Bioengineering, Central South University, Changsha 410083
Abstract To address the problem of learning from incomplete weakly labeled data, a semi-supervised co-training model based on rough set theory is proposed. A semi-supervised discernibility matrix is firstly defined and then used to generate two sufficient and diverse semi-supervised reducts. The base classifiers are trained on the labeled data with two reducts, and then the two classifiers are learned from each other on the unlabeled data by labeling the confident unlabeled examples to its concomitant until no eligible unlabeled example is available. Experimental results on selected UCI datasets show that the proposed model achieves better performance on incomplete weakly labeled data compared with other models, and the effectiveness of the proposed model is verified.
Fund:Supported by National Natural Science Foundation of China(No.61573248,61672358,61703283,61773328), China Postdoctoral Science Foundation(No.2016M590812,2017M612736,2017T 100645), Natural Science Foundation of Guangdong Province(No.2017A030310067)
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2018, Vol. 31 
