不完备弱标记数据的粗糙协同学习模型

doi:10.16451/j.cnki.issn1003-6059.201810009

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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.

Received: 15 April 2018

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TP 18

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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	GAO Can
	ZHOU Jie
	GAO Tianyu
	LAI Zhihui

Cite this article:

GAO Can,ZHOU Jie,GAO Tianyu等. Rough Co-training Model for Incomplete Weakly Labeled Data[J]. , 2018, 31(10): 950-957.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201810009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I10/950

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