I. Soboroff and C. Nicholas, Combining content and collaboration in text filtering, IJCAI99 Workshop: Machine Learning for Information Filtering, pp.86-91, 1999.

Y. Liu, C. Jiang, and H. Zhao, Using contextual features and multi-view ensemble learning in product defect identification from online discussion forums, J]. Decision Support Systems, vol.105, pp.1-12, 2018.

M. Kang, J. Ahn, and K. Lee, Opinion mining using ensemble text hidden Markov models for text classification

, Expert Systems with Applications, vol.94, pp.218-227, 2018.

Z. Lu, W. Liu, and Y. Zhou, An Effective Approach for Chinese News Headline Classification Based on Multi-representation Mixed Model with Attention and Ensemble Learning, National CCF Conference on Natural Language Processing and Chinese Computing, pp.339-350, 2017.

R. Schapire, Y. Singer, and A. Singhal, Boosting and Rocchio applied to text filtering, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pp.215-223, 1998.

J. J. Rocchio, The SMART retrieval system: experiments in automatic document processing, pp.313-323, 1971.

R. E. Schapire, The strength of weak learnability

, Machine Learning, vol.5, pp.197-227, 1990.

M. Galar, A. Fernandez, and E. Barrenechea, A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches

, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 42.4, pp.463-484, 2012.

Y. Freund and R. E. Shapire, Experiments With a New Boosting Algorithm. In: 13th ICML, pp.148-156, 1996.

L. Breiman, Bagging predictors

, Machine Learning, vol.24, pp.123-140, 1996.

G. Salton and C. Buckley, Term-weighting approaches in automatic text retrieval, vol.24, pp.513-523, 1988.

Z. Zheng, X. Wu, and R. Srihari, Feature selection for text categorization on imbalanced data

, ACM Sigkdd Explorations Newsletter. 6, vol.1, pp.80-89, 2004.

T. Rong, H. Gong, . Wing, and . Ng, Stochastic sensitivity oversampling technique for imbalanced data, International Conference on Machine Learning and Cybernetics, pp.161-171, 2014.