Abstract
ML-\(k\)NN is a well-known algorithm for multi-label classification. Although effective in some cases, ML-\(k\)NN has some defect due to the fact that it is a binary relevance classifier which only considers one label every time. In this paper, we present a new method for multi-label classification, which is based on lazy learning approaches to classify an unseen instance on the basis of its \(k\) nearest neighbors. By introducing the coupled similarity between class labels, the proposed method exploits the correlations between class labels, which overcomes the shortcoming of ML-\(k\)NN. Experiments on benchmark data sets show that our proposed Coupled Multi-Label \(k\) Nearest Neighbor algorithm (CML-\(k\)NN) achieves superior performance than some existing multi-label classification algorithms.
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References
Boriah, S., Chandola, V., Kumar, V.: Similarity measures for categorical data: A comparative evaluation. red 30(2), 3 (2008)
Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recognition 37(9), 1757–1771 (2004)
Brinker, K., Hüllermeier, E.: Case-based multilabel ranking. In: IJCAI, pp. 702–707 (2007)
Cao, L.: Coupling learning of complex interactions. Information Processing & Management (2014)
Cao, L.: Non-iidness learning in behavioral and social data. The Computer Journal 57(9), 1358–1370 (2014)
Cao, L., Ou, Y., Yu, P.S.: Coupled behavior analysis with applications. IEEE Transactions on Knowledge and Data Engineering 24(8), 1378–1392 (2012)
Cheng, W., Hüllermeier, E.: Combining instance-based learning and logistic regression for multilabel classification. Machine Learning 76(2–3), 211–225 (2009)
Liu, C., Cao, L., Yu, P.S.: Coupled fuzzy k-nearest neighbors classification of imbalanced non-iid categorical data. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 1122–1129. IEEE (2014)
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Machine Learning 85(3), 333–359 (2011)
Schapire, R.E., Singer, Y.: Boostexter: A boosting-based system for text categorization. Machine Learning 39(2–3), 135–168 (2000)
Spyromitros, E., Tsoumakas, G., Vlahavas, I.P.: 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)
Tsoumakas, G., Katakis, I.: Multi-label classification: An overview. International Journal of Data Warehousing and Mining (IJDWM) 3(3), 1–13 (2007)
Vembu, S., Gärtner, T.: Label ranking algorithms: A survey. In: Preference learning, pp. 45–64. Springer (2011)
Wang, C., Cao, L., Wang, M., Li, J., Wei, W., Ou, Y.: Coupled nominal similarity in unsupervised learning. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 973–978. ACM (2011)
Wieczorkowska, A., Synak, P., Raś, Z.W.: Multi-label classification of emotions in music. In: Intelligent Information Processing and Web Mining, pp. 307–315. Springer (2006)
Zhang, M.L., Zhou, Z.H.: Multilabel neural networks with applications to functional genomics and text categorization. IEEE Transactions on Knowledge and Data Engineering 18(10), 1338–1351 (2006)
Zhang, M.L., Zhou, Z.H.: Ml-knn: A lazy learning approach to multi-label learning. Pattern Recognition 40(7), 2038–2048 (2007)
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Liu, C., Cao, L. (2015). A Coupled k-Nearest Neighbor Algorithm for Multi-label Classification. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9077. Springer, Cham. https://doi.org/10.1007/978-3-319-18038-0_14
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DOI: https://doi.org/10.1007/978-3-319-18038-0_14
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