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A multi-label classification framework using the covering based decision table

Pham, Thanh-Huyen and Van-Tuan, Phan and Thi-Ngan, Pham and Thi-Hong, Vuong and Tri-Thanh, Nguyen and Quang-Thuy, Ha A multi-label classification framework using the covering based decision table. In: 14th Asian Conference on Intelligent Information and Database Systems, Ho Chi Minh City, Vietnam. (In Press)

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Information Technology (IT)
ISI/Scopus indexed conference
Divisions: Faculty of Information Technology (FIT)
Depositing User: Ass. Prof. Tri-Thanh NGUYEN
Date Deposited: 22 Aug 2022 03:58
Last Modified: 22 Aug 2022 03:58

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