TY  - CONF
ID  - SisLab2960
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2960/
A1  - Ha, Quang Thuy
A1  - Pham, Thi Ngan
A1  - Nguyen, Van Quang
A1  - Nguyen, Minh Chau
A1  - Pham, Thanh Huyen
A1  - Nguyen, Tri Thanh
Y1  - 2018/09/05/
N2  - 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.
TI  - o	A new text semi-supervised multi-label learning model based on using the label-feature relations
M2  - Bristol, United Kingdom
AV  - none
T2  - 10th International Conference on Computational Collective Intelligence (ICCCI 2018)
ER  -