@inproceedings{SisLab2316,
       booktitle = {The 9th Asian Conference on Intelligent Information and Database Systems},
           month = {April},
           title = {MASS: a semi-supervised multi-label classification algorithm with specific features},
          author = {Thi Ngan Pham and Van Quang Nguyen and Duc Trong Dinh and Tri Thanh Nguyen and Quang Thuy Ha},
       publisher = {Springer},
            year = {2017},
         journal = {The 9th Asian Conference on Intelligent Information and Database Systems (ACIIDS) 2017},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2316/},
        abstract = {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.}
}