Abstract
In the field of multi-label learning, ML-kNN is the first lazy learning approach and one of the most influential approaches. The main idea of it is to adapt k-NN method to deal with multi-label data, where maximum a posteriori rule is utilized to adaptively adjust decision boundary for each unseen instance. In ML-kNN, all test instances which get the same number of votes among k nearest neighbors have the same probability to be assigned a label, which may cause improper decision since it ignores the local difference of samples. Actually, in real world data sets, the instances with (or without) label l from different locations may have different numbers of neighbors with the label l. In this paper, we propose a locally adaptive Multi-Label k-Nearest Neighbor method to address this problem, which takes the local difference of samples into account. We show how a simple modification to the posterior probability expression, previously used in ML-kNN algorithm, allows us to take the local difference into account. Experimental results on benchmark data sets demonstrate that our approach has superior classification performance with respect to other kNN-based algorithms.
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Notes
- 1.
The code available at https://github.com/DENGBAODAGE/LAMLKNN.
- 2.
Data sets were downloaded from http://mulan.sourceforge.net/datasets.html and http://meka.sourceforge.net/#datasets.
References
Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., Xu, W.: CNN-RNN: a unified framework for multi-label image classification. In: Proceedings of CVPR, pp. 2285–2294 (2016)
Nam, J., Kim, J., Loza Mencía, E., Gurevych, I., Fürnkranz, J.: Large-scale multi-label text classification — revisiting neural networks. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8725, pp. 437–452. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44851-9_28
Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Proceedings of NIPS, vol. 14, pp. 681–687 (2001)
Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)
Tsoumakas, G., Katakis, I., Taniar, D.: Multi-label classification: an overview. Int. J. Data Warehous. Min. 3(3), 1–13 (2007)
Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognit. 40(7), 2038–2048 (2007)
Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theor. 13(1), 21–27 (1967)
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333 (2011)
Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73(2), 133–153 (2008)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-09823-4_34
Zhang, M.L.: LIFT: multi-label learning with label-specific features. IJCAI 37, 1609–1614 (2011)
Zhang, M.L., Zhou, Z.H.: Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18(10), 1338–1351 (2006)
Zhang, M.L.: ML-RBF: RBF neural networks for multi-label learning. Neural Process. Lett. 29(2), 61–74 (2009)
Younes, Z., Abdallah, F., Denoeux, T., Snoussi, H.: A dependent multilabel classification method derived from the k-nearest neighbor rule. EURASIP J. Adv. Signal Process. 2011(1), 1–14 (2011)
Wang, H., Ding, C. H. Q., Huang, H.: Multi-label classification: inconsistency and class balanced k-nearest neighbor. In: Proceedings of AAAI (2010)
Spyromitros, E., Tsoumakas, G., Vlahavas, I.: An empirical study of lazy multilabel classification algorithms. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds.) SETN 2008. LNCS (LNAI), vol. 5138, pp. 401–406. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87881-0_40
Wu, X.Z., Zhou, Z.H.: A unified view of multi-label performance measures. arXiv preprint arXiv: 1609.00288 (2016)
Chiang, T.H., Lo, H.Y., Lin, S.D.: A ranking-based KNN approach for multi-label classification. In: Proceedings of ACML, pp. 81–96 (2012)
Acknowledgement
It was supported by NSF Chongqing China (cstc2017zdcy-zdyf0366). Li Li is the corresponding author for the paper.
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Wang, D., Wang, J., Hu, F., Li, L., Zhang, X. (2018). A Locally Adaptive Multi-Label k-Nearest Neighbor Algorithm. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10937. Springer, Cham. https://doi.org/10.1007/978-3-319-93034-3_7
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