Abstract
Multi-label classification with multiple data views is a recent research field not much explored. This more flexible learning approach allows each pattern to be represented by several sets of attributes and each pattern can have simultaneously associated several labels. In this work, an ensemble-based approach, which enables the fusion of views at decision level by majority voting, is proposed. The study carried out on four data sets considering 27 multi-label evaluation metrics shows that our proposal overcomes and improves the results obtained by the individual views as well as the execution time and the performance of the classic approach which concatenates all the views in a single set of features.
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Charte, F., Rivera, A.J., Del Jesus, M.J., Herrera, F.: LI-MLC: a label inference methodology for addressing high dimensionality in the label space for multilabel classification. IEEE Trans. Neural Netw. Learn. Syst. 25(10), 1842–1854 (2014)
Chen, Q., Sun, S.: Hierarchical multi-view fisher discriminant analysis. In: Leung, C.-S. Lee, M. Chan, J.H. (eds.) Neural Information Processing, pp. 289–298. Springer, Berlin (2009)
Cheng, W., Hüllermeier, E.: Combining instance-based learning and logistic regression for multilabel classification. Mach. Learn. 76(2–3), 211–225 (2009)
Clare, A., King, R.D.: Knowledge discovery in multi-label phenotype data. In: Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 42–53. PKDD ’01, Springer-Verlag, London, UK (2001)
Elisseeff, A., Weston, J.: Kernel methods for multi-labelled classification and categorical regression problems. Adv. Neural Inform. Process. Syst. 14, 681–687 (2001)
Farquhar, J., Hardoon, D., Meng, H., Shawe-taylor, J.S., Szedmak, S.: Two view learning: SVM-2K, theory and practice. In: Weiss, Y., Sch ölkopf B., Platt J.C. (eds.) Advances in Neural Information Processing Systems, 18, pp. 355–362. MIT Press (2005)
Fürnkranz, J., Hüllermeier, E., Mencía, E.L., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73(2), 133–153 (2008)
Gibaja, E., Ventura, S.: Multi-label learning: a review of the state of the art and ongoing research. Wiley Interdiscip. Rev. Data Mining Knowl. Discov. 4(6), 411–444 (2014)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)
He, J., Lawrence, R.: A graph-based framework for multi-task multi-view learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 25–32 (2011)
Jin, X., Zhuang, F., Wang, S., He, Q., Shi, Z.: Shared structure learning for multiple tasks with multiple views. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science, vol. 8189, pp. 353–368. Springer, Berlin (2013)
Luo, Y., Tao, D., Xu, C., Xu, C., Liu, H., Wen, Y.: Multiview vector-valued manifold regularization for multilabel image classification. IEEE Trans. Neural Netw. Learn. Syst. 24(5), 709–722 (2013)
Madjarov, G., Kocev, D., Gjorgjevikj, D., Džeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recognit. 45(9), 3084–3104 (2012)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 1–27 (2011)
Read, J.: A pruned problem transformation method for multi-label classification. In: Proceedings of the NZ Computer Science Research Student Conference, pp. 143–150 (2008)
Sechidis, K., Tsoumakas, G., Vlahavas, I.: On the stratification of multi-label data. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) Machine Learning and Knowledge Discovery in Databases, pp. 145–158. Springer, Berlin (2011)
Snoek, C.G., Worring, M., Smeulders, A.W.: Early versus late fusion in semantic video analysis. In: Proceedings of the 13th annual ACM international conference on Multimedia, pp. 399–402. ACM (2005)
Sun, S.: A survey of multi-view machine learning. Neural Comput. Appl. 23(7–8), 2031–2038 (2013)
Sun, S., Zhang, Q.: Multiple-view multiple-learner semi-supervised learning. Neural Process. Lett. 34(3), 229–240 (2011)
Szedmak, S., Shawe-Taylor, J.: Synthesis of maximum margin and multiview learning using unlabeled data. Neurocomputing 70(7), 1254–1264 (2007)
Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.: Multi-label classification of music into emotions. EURASIP J. Audio Speech Music Process. 2011(1), 4 (2011)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsets for multi-label classification. IEEE Trans. Knowl. Data Eng. 23(7), 1079–1089 (2010)
Tsoumakas, G., Dimou, A., Spyromitros, E., Mezaris, V., Kompatsiaris, I., Vlahavas, I.: Correlation-based pruning of stacked binary relevance models for multi-label learning. In: Proceedings of the 1st International Workshop on Learning from Multi-Label Data, pp. 101–116 (2009)
Tsoumakas, G., Spyromitros-Xioufis, E., Vilcek, J., Vlahavas, I.: Mulan: a java library for multi-label learning. J. Mach. Learn. Res. 12, 2411–2414 (2011)
Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992)
Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. ArXiv.org (Cornell University) (2013)
Xu, J.: Fast multi-label core vector machine. Pattern Recognit. 46(3), 885–898 (2013)
Xu, J.: Laboratory of Intelligent computation. http://computer.njnu.edu.cn/Lab/LABIC/LABIC_Software.html (2013)
Xu, Z., Sun, S.: An algorithm on multi-view adaboost. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) Neural Information Processing. Theory and Algorithms, pp. 355–362. Springer, Berlin (2010)
Zhang, J., Huan, J.: Inductive multi-task learning with multiple view data. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 543–551. ACM (2012)
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., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognit. 40(7), 2038–2048 (2007)
Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)
Zhang, Q., Sun, S.: Multiple-view multiple-learner active learning. Pattern Recognit. 43(9), 3113–3119 (2010)
Zhou, T., Tao, D., Wu, X.: Compressed labeling on distilled labelsets for multi-label learning. Mach. Learn. 88(1–2), 69–126 (2012)
Zou, F., Liu, Y., Wang, H., Song, J., Shao, J., Zhou, K., Zheng, S.: Multi-view multi-label learning for image annotation. Multimed. Tools Appl. 1–18 (2015). doi:10.1007/s11042-014-2423-2
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This work has been supported by the Spanish Ministry of Science and Technology project TIN2014-55252-P and FEDER funds.
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Gibaja, E.L., Moyano, J.M. & Ventura, S. An ensemble-based approach for multi-view multi-label classification. Prog Artif Intell 5, 251–259 (2016). https://doi.org/10.1007/s13748-016-0098-9
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DOI: https://doi.org/10.1007/s13748-016-0098-9