@inproceedings{SisLab2960,
       booktitle = {10th International Conference on Computational Collective Intelligence (ICCCI 2018)},
           month = {September},
           title = {o	A new text semi-supervised multi-label learning model based on using the label-feature relations},
          author = {Quang Thuy Ha and Thi Ngan Pham and Van Quang Nguyen and Minh Chau Nguyen and Thanh Huyen Pham and Tri Thanh Nguyen},
            year = {2018},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2960/},
        abstract = {Multi-label learning has become popular and omnipresent in many real-world problems, especially in text classification applications, in which an instance could belong to different classes simultaneously. Due to these label constraints, there are some challenges occurring in building multi-label data. Semi-supervised learning is one possible approach to exploit abundantly unlabeled data for enhancing the classification performance with a small labeled dataset. In this paper, we propose a solution to select the most influential label based on using the relations among the labels and features to a semi-supervised multi-label classification algorithm on texts. Experiments on two datasets of Vietnamese reviews and English emails of Enron show the positive effects of the proposal.}
}