Abstract
Multi-label classification, where each instance is assigned with multiple labels, has been an attractive research topic in data mining. The annotations of multi-label instances are typically more difficult and time consuming, since they are simultaneously associated with multiple labels. Therefore, active learning, which reduces the labeling cost by actively querying the labels of the most valuable data, becomes particularly important for multi-label learning. Study reveals that methods querying instance-label pairs are more effective than those query instances, since for each sample, only some effective labels need to be annotated while others can be inferred by exploring the label correlations. However, with the high dimensionality of label space, the instance-label pair selective algorithm will be affected since the computational cost of training a multi-label model may be strongly affected by the number of labels. In this paper we propose an approach that combines instance sampling with optimal label subset selection, which can effectively improve the classification model performance and substantially reduce the annotation cost. Experimental results demonstrate the superiority of the proposed approach to state-of-the-art methods on three benchmark datasets.
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References
Settles, B.: Active learning literature survey, vol. 52, pp. 55–66. University of Wisconsin, Madison (2010)
Carneiro, G., Chan, A.B., Moreno, P.J., Vasconcelos, N.: Supervised learning of semantic classes for image annotation and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(3), 394–410 (2007)
Qi, G.J., Hua, X.S., Rui, Y., Tang, J., Mei, T., Zhang, H.J.: Correlative multi-label video annotation. In: Proceedings of the 15th International Conference on Multimedia, pp. 17–26. ACM (2007)
Yang, Y., Wu, F., Nie, F., Shen, H.T., Zhuang, Y., Hauptmann, A.G.: Web and personal image annotation by mining label correlation with relaxed visual graph embedding. IEEE Transactions on Image Processing 21(3), 1339–1351 (2012)
Zhang, M., Zhou, Z.: A review on multi-label learning algorithms (2013)
Luo, T., Kramer, K., Samson, S., Remsen, A., Goldgof, D., Hall, L., Hopkins, T.: Active learning to recognize multiple types of plankton. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 478–481. IEEE (2004)
Yan, R., Yang, L., Hauptmann, A.: Automatically labeling video data using multi-class active learning. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 516–523. IEEE (2003)
Li, X., Wang, L., Sung, E.: Multilabel svm active learning for image classification. In: 2004 International Conference on Image Processing, ICIP 2004, vol. 4, pp. 2207–2210. IEEE (2004)
Brinker, K.: On active learning in multi-label classification. In: From Data and Information Analysis to Knowledge Engineering, pp. 206–213. Springer (2006)
Kruse, M.S.R., Nçrnberger, C.B.A., Gaul, W.: From data and information analysis to knowledge engineering
Yang, B., Sun, J.T., Wang, T., Chen, Z.: Effective multi-label active learning for text classification. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 917–926. ACM (2009)
Esuli, A., Sebastiani, F.: Training data cleaning for text classification. In: Azzopardi, L., Kazai, G., Robertson, S., Rüger, S., Shokouhi, M., Song, D., Yilmaz, E. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 29–41. Springer, Heidelberg (2009)
Qi, G.J., Hua, X.S., Rui, Y., Tang, J., Zhang, H.J.: Two-dimensional multilabel active learning with an efficient online adaptation model for image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(10), 1880–1897 (2009)
Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3–12. Springer-Verlag New York, Inc. (1994)
Roy, N., McCallum, A.: Toward optimal active learning through monte carlo estimation of error reduction. In: ICML, Williamstown (2001)
Brinker, K.: Incorporating diversity in active learning with support vector machines. In: ICML, vol. 3, pp. 59–66 (2003)
Nguyen, H.T., Smeulders, A.: Active learning using pre-clustering. In: Proceedings of the Twenty-First International Conference on Machine Learning, vol. 79. ACM (2004)
Donmez, P., Carbonell, J.G., Bennett, P.N.: Dual strategy active learning. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 116–127. Springer, Heidelberg (2007)
Huang, S.J., Jin, R., Zhou, Z.H.: Active learning by querying informative and representative examples. In: Advances in Neural Information Processing Systems, pp. 892–900 (2010)
Vijayanarasimhan, S., Grauman, K.: What’s it going to cost you?: Predicting effort vs. informativeness for multi-label image annotations. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 2262–2269. IEEE (2009)
Kong, X., Ng, M.K., Zhou, Z.H.: Transductive multilabel learning via label set propagation. IEEE Transactions on Knowledge and Data Engineering 25(3), 704–719 (2013)
Rifkin, R., Klautau, A.: In defense of one-vs-all classification. The Journal of Machine Learning Research 5, 101–141 (2004)
Li, X., Guo, Y.: Active learning with multi-label svm classification. In: Proceedings of the Twenty-Third international Joint Conference on Artificial Intelligence, pp. 1479–1485. AAAI Press (2013)
Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Advances in Neural Information Processing Systems, pp. 681–687 (2001)
Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: A new benchmark collection for text categorization research. The Journal of Machine Learning Research 5, 361–397 (2004)
Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recognition 37(9), 1757–1771 (2004)
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Jiao, Y., Zhao, P., Wu, J., Xian, X., Xu, H., Cui, Z. (2014). Active Multi-label Learning with Optimal Label Subset Selection. In: Luo, X., Yu, J.X., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2014. Lecture Notes in Computer Science(), vol 8933. Springer, Cham. https://doi.org/10.1007/978-3-319-14717-8_41
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DOI: https://doi.org/10.1007/978-3-319-14717-8_41
Publisher Name: Springer, Cham
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