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.
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Zhang, X., Bai, B., Li, Y. (2012). Tri-training Based on Neural Network Ensemble Algorithm. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_6
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DOI: https://doi.org/10.1007/978-3-642-31919-8_6
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