Tri-training based on neural network ensemble algorithm

Authors:
Xiaojie Zhang;Bendu Bai;Ying Li
Affiliations:
School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China;School of Telecommunication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an, China;School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

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Abstract

In this paper, the neural network ensemble algorithm is proposed to solve the problem of the mislabeled data in the tri-training process. Firstly, we analyze the advantage of the neural network ensemble, and then introduce it to correct the mislabeled data to improve the quality of the enlarged training set, so the precision and generalization of learns is improved. Experimental results on UCI data sets indicate that the classification performance of the proposed algorithm is 22.87% higher than that of the tri-training algorithm under the four kinds of the unlabeled rates. The proposed algorithm could effectively exploit unlabeled data to enhance the learning performance.