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MASS: a semi-supervised multi-label classification algorithm with specific features

Pham, Thi Ngan and Nguyen, Van Quang and Dinh, Duc Trong and Nguyen, Tri Thanh and Ha, Quang Thuy (2017) MASS: a semi-supervised multi-label classification algorithm with specific features. In: The 9th Asian Conference on Intelligent Information and Database Systems, 3-5 April 2017, Kanazawa, Japan.

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Information Technology (IT)
Divisions: Faculty of Information Technology (FIT)
Depositing User: Ass. Prof. Tri-Thanh NGUYEN
Date Deposited: 13 Jan 2017 02:33
Last Modified: 04 Dec 2017 09:32

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