TY  - CONF
ID  - SisLab2316
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2316/
A1  - Pham, Thi Ngan
A1  - Nguyen, Van Quang
A1  - Dinh, Duc Trong
A1  - Nguyen, Tri Thanh
A1  - Ha, Quang Thuy
Y1  - 2017/04//
N2  - Multi-Label Classification (MLC), which, recently, has attracted several attentions, aims at building classification models for objects assigned with multiple class labels simultaneously. Existing approaches for MLC mainly focus on improving supervised learning which needs a relatively large amount of labeled training data. In this paper, we propose a semi-supervised algorithm to exploit unlabeled data for MLC for enhancing the performance. In the training process, our algorithm exploits the specific features per prominent class label chosen by a greedy approach as an extension of LIFT algorithm, and unlabeled data consumption mechanism from TESC. In classification, the 1-Nearest-Neighbor (1NN) is applied to select appropriate class labels for a new data instance. Our experimental results on a data set of hotel (for tourism) reviews indicate that a reasonable amount of unlabelled data helps to increase the F1 score. Interestingly, with a small amount of labelled data, our algorithm can reach comparative performance to a larger amount of labelled data.
PB  - Springer
TI  - MASS: a semi-supervised multi-label classification algorithm with specific features
M2  - Kanazawa, Japan
AV  - none
T2  - The 9th Asian Conference on Intelligent Information and Database Systems
ER  -