%0 Conference Paper
%A Ha, Quang Thuy
%A Pham, Thi Ngan
%A Nguyen, Van Quang
%A Nguyen, Minh Chau
%A Pham, Thanh Huyen
%A Nguyen, Tri Thanh
%B 10th International Conference on Computational Collective Intelligence (ICCCI 2018)
%C Bristol, United Kingdom
%D 2018
%F SisLab:2960
%T o	A new text semi-supervised multi-label learning model based on using the label-feature relations
%U https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2960/
%X 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.