@inproceedings{SisLab4751,
       booktitle = {14th Asian Conference on Intelligent Information and Database Systems},
           title = {A multi-label classification framework using the covering based decision table},
          author = {Thanh-Huyen Pham and Phan Van-Tuan and Pham Thi-Ngan and Vuong Thi-Hong and Nguyen Tri-Thanh and Ha Quang-Thuy},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4751/},
        abstract = {Multi-label classification (MLC) has recently drawn much attention thanks to its usefulness and omnipresence in real-world applications, in which objects may be characterized by more than one labels. One of the challenges in MLC is to deter-mine the relationship between the labels due to the fact that there is not any as-sumptions of the independence between labels, and there is not any information and knowledge about these relationships in a training dataset. Recently, many re-searches have focused on exploiting these label relationships to enhance the per-formance of the classification, however there have not many of them using the covering rough set. This paper propose a multi-label classification algorithm named CDTML, based on ML-KNN algorithm, using covering based decision table which exploits the relationship between labels to enhance the performance of the multi-label classifier. The experimental results on serveral dataset of Enron, Medical and a Vietnamese dataset of hotel reviews shown the effectiveness of the proposed algorithm.}
}