Abstract
Multi-label classification is a special learning task where each instance may be associated with multiple labels simultaneously. There are two main challenges: (a) discovering and exploiting the label correlations automatically, and (b) separating the relevant labels from the irrelevant labels of each instance effectively. Nevertheless, many existing multi-label classification algorithms fail to deal with both challenges at the same time. In this paper, we integrate multi-label classification, label correlations and threshold calibration into a unified learning framework, and propose calibrated multi-label classification with label correlations, named CMLLC. Specifically, we firstly introduce a label covariance matrix to characterize the label correlations and a virtual label to calibrate label decision threshold of each instance. Secondly, the framework of our CMLLC model is constructed for joint learning of the label correlations and model parameters corresponding to each label and the virtual label. Lastly, the optimization problem is jointly convex and solved by an alternating iterative method. Experimental results on sixteen multi-label benchmark datasets in terms of five evaluation criteria demonstrate that CMLLC outperforms the state-of-the-art multi-label classification algorithms.
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Schapire RE, Singer Y (2000) Boostexter: a boosting-based system for text categorization. Mach Learn 39(2/3):135–168
Zhang ML, Zhou ZH (2006) Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans Knowl Data Eng 18(10):1338–1351
Yan Y, Wang Y, Gao WC (2018) LSTM: multi-label ranking for document classification. Neural Process Lett 47(1):117–138
Boutell MR, Luo J, Luo JB, Shen XP, Brown CM (2004) Learning multi-label scene classification. Pattern Recognit 37(9):1757–1771
Jiang A, Wang C, Zhu Y (2008) Calibrated rank-SVM for multi-label image categorization. In: Proceedings of the international joint conference on neural networks, Hong Kong, China, pp 1450–1455
Liu W, Yang X, Tao D (2018) Multiview dimension reduction via Hessian multiset canonical correlations. Inf Fusion 41:119–128
Yu J, Zhang B, Kuang Z (2017) iPrivacy: image privacy protection by identifying sensitive objects via deep multi-task learning. IEEE Trans Inf Forensics Secur 12(5):1005–1016
Yu J, Yang X, Gao F (2017) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Trans Cybern 47(12):4014–4024
Tao D, Hong C, Yu J (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659–5670
Trohidis K, Tsoumakas G, Kalliris G, Vlahavas IP (2008) Multilabel classification of music into emotions. In: Proceedings of the 9th international conference on music information retrieval, Philadephia, PA, USA, pp 325–330
Zhang ML, Zhou ZH (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837
Huang SJ, Yu Y, Zhou ZH (2012) Multi-label hypothesis reuse. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, Beijing, China, pp 525–533
Elisseeff A, Weston J (2001) A kernel method for multi-labelled classification. In: Proceedings of the 14th conference on neural information processing systems (NIPS2001), Vancouver, British Columbia, Canada, pp 681–687
Zhang ML, Pena JM, Robles V (2009) Feature selection for multi-label naive Bayes classification. Inf Sci 179(19):3218–3229
Zhang ML (2009) ML-RBF: RBF neural networks for multi-label learning. Neural Process Lett 29(2):61–74
Read J, Pfahringer B, Holmes G, Frank E (2011) Classifier chains for multi-label classification. Mach Learn 85(3):333–359
Tsoumakas G, Katakis I, Vlahavas I (2008) Effective and efficient multilabel classification in domains with large number of labels. In: Proceedings of ECML/PKDD 2008 workshop on mining multidimensional data, Antwerp, Belgium, pp 30–44
Ghamrawi N, Mccallum A (2005) Collective multilabel classification. In: Proceedings of the 14th ACM international conference on information and knowledge management, Bremen, Germany, pp 195–200
Godbole S, Sarawagi S (2004) Discriminative methods for multi-labeled classification. In: Dai H, Srikant R, Zhang C (eds) Lecture Notes in Artificial Intelligence, vol 3056. Springer, Berlin, pp 22–30
Chen G, Song YQ, Wang F, et al (2008) Semi-supervised multi-label learning by solving a Sylvester equation. In: SIAM conference on data mining, Atlanta, Georgia, pp 410–419
Gu Q, Li Z, Han J (2011) Correlated multi-label feature selection. In: Proceedings of the 20th ACM international conference on information and knowledge management, Glasgow, Scotland, UK, pp 1087–1096
Zhang Y, Yeung DY (2013) Multilabel relationship learning. ACM Trans Knowl Discov Data 7(2):1–30
Zhu Y, Kwok JT, Zhou ZH (2017) Multi-Label Learning with Global and Local Label Correlation. IEEE Trans Knowl Data Eng, arXiv preprint, arXiv:1704:01415
He ZF, Yang M, Liu HD (2014) Joint learning of multi-label classification and label correlations. J Softw 25(9):1967–1981 (in Chinese)
Hullermeier E, Furnkranz J, Cheng W, Brinker K (2008) Label ranking by learning pairwise preferences. Artif Intell 172(16):1897–1916
Furnkranz F, Hullermeier E, Mencia EL, Brinker K (2008) Multilabel classification via calibrated label ranking. Mach Learn 73(2):133–153
Read J (2008) A pruned problem transformation method for multi-label classification. In: Proceedings of New Zealand computer science research student conference, Christchurch, New Zealand, pp 143–150
Tsoumakas G, Katakis I, Vlahavas I (2011) Random k-labelsets for multilabel classification. IEEE Trans Knowl Data Eng 23(7):1079–1089
Gharroudi O, Elghazel H, Aussem A (2015) Calibrated k-labelsets for ensemble multi-label classification. In: Proceedings of international conference on neural information processing, pp 573–582
He ZF, Yang M, Liu HD (2015) Multi-task joint feature selection for multi-label classification. Chin J Electron 24(CJE–2):281–287
Sun Z, Zhao Y, Cao D, Hao H (2017) Hierarchical multilabel classification with optimal path prediction. Neural Process Lett 45(1):263–277
Xu J (2012) An efficient multi-label support vector machine with a zero label. Expert Syst Appl 39(5):4796–4804
Xu J (2014) Multi-label core vector machine with a zero label. Pattern Recognit 47(7):2542–2557
Clare A, King RD (2001) Knowledge discovery in multi-label phenotypedata. In: Raedt LD, Siebes A (eds) Lecture Notes in Computer Science. Springer, Berlin, pp 42–53
Zhang ML, Zhou ZH (2007) ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognit 40(7):2038–2048
Kwok JT (1999) Moderating the outputs of support vector machine classifiers. IEEE Trans Neural Netw 10(5):1018–1031
Xu J (2013) Fast multi-label core vector machine. Pattern Recognit 46(3):885–898
Zhang Y, Yeung DY (2010) A convex formulation for learning task relationships in multi-task learning. In: Proceedings of the 26th conference on uncertainty in artificial intelligence, Catalina Island, California, pp 733–742
Chao G, Sun S (2016) Consensus and complementarity based maximum entropy discrimination for multi-view classification. Inf Sci 367–368:296–310
Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, New York
Chen J, Ye J (2008) Training SVM with indefinite kernels. In: Proceedings of the 25th international conference on machine learning, Helsinki, Finland, pp 136–143
Tsoumakas G, Xioufis ES, Vilcek J (2011) Mulan: a java library for multi-label learning. J Mach Learn Res 12(7):2411–2414
Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30
Acknowledgements
This work was supported by National Natural Science Foundation of China under Grants 61876087, 61502058, the State Key Program of National Natural Science Foundation of China under Grant 61432008, the Science and Technology Research Project of Jiangxi Provincial Education Department under Grant GJJ151262, Natural Science Foundation of Educational Committee of Jiangsu Province under Grant 15KJB520002, and the Social Science Research Project of Pingxiang under Grant 2017XW02. The authors would like to thank the anonymous reviewers and the editors for their helpful comments and suggestions.
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He, ZF., Yang, M., Liu, HD. et al. Calibrated Multi-label Classification with Label Correlations. Neural Process Lett 50, 1361–1380 (2019). https://doi.org/10.1007/s11063-018-9925-2
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DOI: https://doi.org/10.1007/s11063-018-9925-2