%A Thanh-Huyen Pham
%A Phan Van-Tuan
%A Pham Thi-Ngan
%A Vuong Thi-Hong
%A Nguyen Tri-Thanh
%A Ha Quang-Thuy
%T A multi-label classification framework using the covering based decision table
%X 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.
%C Ho Chi Minh City, Vietnam
%L SisLab4751