Abstract
Multi-label Classification (MLC), which recently has attracted numerous 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 data for training. In this work, we propose a semi-supervised MLC algorithm to exploit unlabeled data 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 unlabeled data helps to increase the F1 score. Interestingly, with a small amount of labeled data, our algorithm can reach comparative performance to a larger amount of labeled data.
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References
Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. NIPS 2001, 681–687 (2001)
Rousu, J., Saunders, C., Szedmák, S., Shawe-Taylor, J.: Kernel-based learning of hierarchical multilabel classification models. J. Mach. Learn. Res. 7, 1601–1626 (2006)
Silla Jr., C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Min. Knowl. Discov. (DATAMINE) 22(1–2), 31–72 (2011)
Tsoumakas, G., Katakis, I., Vlahavas, I.P.: Mining Multi-label Data. Data Min. Knowl. Discov. Handb. 2010, 667–685 (2010)
Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.P.: Multi-label classification of music into emotions. ISMIR 2008, 325–330 (2008)
Zhang, M.-L., Zhou, Z.-H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognit. (PR) 40(7), 2038–2048 (2007)
Zhang, M.-L., Lei, W.: LIFT: multi-label learning with label-specific features. IEEE Trans. Pattern Anal. Mach. Intell. 37(1), 107–120 (2015)
Zhang, J.-J., Fang, M., Li, X.: Multi-label learning with discriminative features for each label. Neurocomputing 154, 305–316 (2015)
Huaqiao, Q., Zhang, S., Liu, H., Zhao, J.: A multi-label classification algorithm based on label-specific features. Wuhan Univ. J. Natl. Sci. 16(6), 520–524 (2011)
Basu, S.: Semi-supervised clustering: probabilistic models, algorithms and experiments. University of Texas at Austin (2005)
Tian, D.: Semi-supervised learning for refining image annotation based on random walk model. Knowl. Based Syst. 72–80 (2014)
Dara, R., Kermer, S., Stacey, D.: Clustering unlabeled data with SOMs improves classification of labeled real-world data. In: Proceedings of the 2002 International Joint Conference on Neural Networks, pp. 2237–2242 (2002)
Luo, X., Liu, F., Yang, S., Wang, X., Zhou, Z.: Joint sparse regularization based sparse semi-supervised extreme learning machine (S3ELM) for classification. Knowl. Based Syst. 73, 149–160 (2015)
Demirez, A., Bennett, K., Embrechts, M.: Semi-supervised clustering using genetic algorithms. In: Proceedings of Artificial Neural Networks in Engineering (ANNIE-99), pp. 809–814 (1999)
Zhang, W., Tang, X., Yoshida, T.: TESC: An approach to text classification using semi-supervised clustering. Knowl. Based Syst. 75, 152–160 (2015)
Kong, X., Ng, M.K., Zhou, Z.-H.: Transductive multilabel learning via label set propagation. IEEE Trans. Knowl. Data Eng. 25(3), 704–719 (2013)
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This work was supported in part by VNU Grant QG-15-22.
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Pham, TN., Nguyen, VQ., Dinh, DT., Nguyen, TT., Ha, QT. (2017). MASS: A Semi-supervised Multi-label Classification Algorithm with Specific Features. In: Król, D., Nguyen, N., Shirai, K. (eds) Advanced Topics in Intelligent Information and Database Systems. ACIIDS 2017. Studies in Computational Intelligence, vol 710. Springer, Cham. https://doi.org/10.1007/978-3-319-56660-3_4
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DOI: https://doi.org/10.1007/978-3-319-56660-3_4
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