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Data Analysis and Knowledge Discovery
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The Research on Semi-Supervised Text Classification Method Based on DW-TCI
Yu Bengong,Ji Haomin
(School of Management, Hefei University of Technology, Hefei 230009, China)
(Key Laboratory of Process Optimization & Intelligent Decision-making, Ministry of Education, Hefei University of Technology, Hefei 230009, China)
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[Objective] To efficiently classify text with only a small number of annotations, and propose a new semi-supervised text classification method.

[Methods] The proposed DW-TCI semi-supervised text classification method uses two-channel feature extraction to obtain two sets of feature input vectors of the base classifier group, and introduces the semi-supervised classification method based on divergence and the idea of integrated learning. The result sample is introduced into the model training, and finally the classification result of the predicted text is obtained by the equivalent weighted voting method.

[Result] Under two different data sets, when the DW-TCI method is trained with 20% labeled samples, the classification accuracy reaches 92.32% and 87.01% respectively, which is at least 5.54% and 5.65% higher than other semi-supervised classification methods.

[Limitations] Text uses a smaller number of data sets and has not been verified on more data sets.

[Conclusion] The semi-supervised classification method in this paper can greatly reduce the labeling of training samples and provide effective support for service providers to perform efficient text classification.

Key words semi-supervised classification      sample divergence      classifier divergence      ensemble learning      DW-TCI      
Published: 28 July 2020
ZTFLH:  TP391  

Cite this article:

Yu Bengong, Ji Haomin. The Research on Semi-Supervised Text Classification Method Based on DW-TCI . Data Analysis and Knowledge Discovery, 0, (): 1-.

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