Semi-Supervised Method for Text Classification Based on DW-TCI
Yu Bengong1,2,Ji Haomin1()
1School of Management, Hefei University of Technology, Hefei 230009, China 2Key Laboratory of Process Optimization & Intelligent Decision-Making, Ministry of Education, Hefei University of Technology, Hefei 230009, China
[Objective] This paper proposes a new semi-supervised method for text classification, aiming to efficiently process texts with only small amount of annotations.[Methods] The proposed DW-TCI based method used double-channel feature extraction to obtain two sets of feature input vectors of the base classifier group. Then, we introduced the semi-supervised classification method with divergence and the idea of integrated learning. Finally, we trained the non-supervised sample with our model, and obtained the classification result of the predicted text with the equivalent weighted voting method.[Results] We examined our method with two different data sets having 20% labeled samples. The classification accuracy reached 92.32% and 87.01%, which were at least 5.54% and 5.65% higher than those of similar methods.[Limitations] The sample data set needs to be expanded.[Conclusions] The proposed method could reduce the labeling workloads of training samples and provide effective support for better text classification results.
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Yu Bengong,Ji Haomin. Semi-Supervised Method for Text Classification Based on DW-TCI. Data Analysis and Knowledge Discovery, 2020, 4(10): 58-69.
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