Abstract
In multi-label classification, the task is to induce predictive models which can assign a set of relevant labels for the unseen instance. The strategy of label-specific features has been widely employed in learning from multi-label examples, where the classification model for predicting the relevancy of each class label is induced based on its tailored features rather than the original features. Existing approaches work by generating a group of tailored features for each class label independently, where label correlations are not fully considered in the label-specific features generation process. In this article, we extend existing strategy by proposing a simple yet effective approach based on BiLabel-specific features. Specifically, a group of tailored features is generated for a pair of class labels with heuristic prototype selection and embedding. Thereafter, predictions of classifiers induced by BiLabel-specific features are ensembled to determine the relevancy of each class label for unseen instance. To thoroughly evaluate the BiLabel-specific features strategy, extensive experiments are conducted over a total of 35 benchmark datasets. Comparative studies against state-of-the-art label-specific features techniques clearly validate the superiority of utilizing BiLabel-specific features to yield stronger generalization performance for multi-label classification.
- J. Amores. 2013. Multiple instance classification: Review, taxonomy and comparative study. Artificial Intelligence 201 (2013), 81–105. Google ScholarDigital Library
- R. Babbar and B. Schölkopf. 2019. Data scarcity, robustness and extreme multi-label classification. Machine Learning 108, 8--9 (2019), 1329–1351.Google ScholarDigital Library
- K. Bhatia, H. Jain, P. Kar, M. Varma, and P. Jain. 2018. Sparse local embeddings for extreme multi-label classification. In Proceedings of the 28th International Conference on Advances in Neural Information Processing Systems. C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett (Eds.). MIT Press, Cambridge, MA, 730–738. Google ScholarDigital Library
- M. Boutell, J. Luo, X. Shen, and C. M. Brown. 2004. Learning multi-label scene classification. Pattern Recognition 37, 9 (2004), 1757–1771.Google ScholarCross Ref
- C. Brinker, E. Loza Mencía, and J. Fürnkranz. 2014. Graded multilabel classification by pairwise comparisons. In Proceedings of the 14th IEEE International Conference on Data Mining. 731–736. Google ScholarDigital Library
- S. Canuto, M. A. Gonçalves, and F. Benevenuto. 2016. Exploiting new sentiment-based meta-level features for effective sentiment analysis. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining. 53–62. Google ScholarDigital Library
- M.-A. Carbonneaua, V. Cheplyginabc, E. Granger, and G. Gagnon. 2018. Multiple instance learning: A survey of problem characteristics and applications. Pattern Recognition 77 (2018), 329–353. Google ScholarDigital Library
- C.-C. Chang and C.-J. Lin. 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 3, Article 27 (2011). Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm. Google ScholarDigital Library
- Z.-M. Chen, X.-S. Wei, P. Wang, and Y.-W. Guo. 2019. Multi-label image recognition with graph convolutional networks. In Proceedings of the 32nd IEEE Conference on Computer Vision and Pattern Recognition. 5177–5186.Google ScholarCross Ref
- Z.-S. Chen and M.-L. Zhang. 2019. Multi-label learning with regularization enriched label-specific features. In Proceedings of the 11th Asian Conference on Machine Learning. 411–424.Google Scholar
- J. Demšar. 2006. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1 (2006), 1–30. Google ScholarDigital Library
- J. Feng and Z.-H. Zhou. 2017. Deep MIML network. In Proceedings of the 31st AAAI Conference on Artificial Intelligence. 1884–1890. Google ScholarDigital Library
- J. Fürnkranz, E. Hüllermeier, E. Loza Mencía, and K. Brinker. 2008. Multilabel classification via calibrated label ranking. Machine Learning 73, 2 (2008), 133–153. Google ScholarDigital Library
- E. Gibaja and S. Ventura. 2015. A tutorial on multilabel learning. ACM Computing Surveys 47, 3, Article 52 (2015). Google ScholarDigital Library
- H. Gouk, B. Pfahringer, and M. Cree. 2016. Learning distance metrics for multi-label classification. In Proceedings of the 8th Asian Conference on Machine Learning. 318–333.Google Scholar
- Y. Guo, F. Chung, G. Li, J. Wang, and J. C. Gee. 2019. Leveraging label-specific discriminant mapping features for multi-label learning. ACM Transactions on Knowledge Discovery from Data 13, 2, Article 24 (2019). Google ScholarDigital Library
- J. Huang, G. Li, Q. Huang, and X. Wu. 2016. Learning label-specific features and class-dependent labels for multi-label classification. IEEE Transactions on Knowledge and Data Engineering 28, 12 (2016), 3309–3323. Google ScholarDigital Library
- J. Huang, G. Li, Q. Huang, and X. Wu. 2018. Joint feature selection and classification for multilabel learning. IEEE Transactions on Cybernetics 48, 3 (2018), 876–889.Google ScholarCross Ref
- J. Huang, G. Li, S. Wang, Z. Xue, and Q. Huang. 2017. Multi-label classification by exploiting local positive and negative pairwise label correlation. Neurocomputing 257 (2017), 164–174.Google ScholarCross Ref
- M. Huang, F. Zhuang, X. Zhang, X. Ao, Z. Niu, M.-L. Zhang, and Q. He. 2019. Supervised representation learning for multi-label classification. Machine Learning 108, 5 (2019), 747–763. Google ScholarDigital Library
- S.-J. Huang, G.-X. Li, W.-Y. Huang, and S.-Y. Li. 2020. Incremental multi-label learning with active queries. Journal of Computer Science and Technology 35, 2 (2020), 234–246.Google ScholarDigital Library
- B.-B. Jia and M.-L. Zhang. 2020. Multi-dimensional classification via kNN feature augmentation. Pattern Recognition 106 (2020), 107423.Google ScholarCross Ref
- B.-B. Jia and M.-L. Zhang. 2020. Multi-dimensional classification via stacked dependency exploitation. Science China Information Sciences 63, 12 (2020), 222102.Google ScholarCross Ref
- X.-Y. Jia, S.-S. Zhu, and W.-W. Li. 2020. Joint label-specific features and correlation information for multi-label learning. Journal of Computer Science and Technology 35, 2 (2020), 247–258.Google ScholarCross Ref
- X.-C. Li, D.-C. Zhan, J.-Q. Yang, and Y. Shi. 2021. Deep multiple instance selection. Science China Information Sciences 64, 3, Article 130102 (2021).Google Scholar
- Y. Li, Y. Song, and J. Luo. 2017. Improving pairwise ranking for multi-label image classification. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. 3617–3625.Google Scholar
- W. Liu and I. W. Tsang. 2015. Large margin metric learning for multi-label prediction. In Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2800–2806. Google ScholarDigital Library
- J. Ma, H. Zhang, and T. W. S. Chow. 2021. Multilabel classification with label-specific features and classifiers: A coarse- and fine-tuned framework. IEEE Transactions on Cybernetics 15, 2 (2021), 1028–1042.Google ScholarCross Ref
- Y. Ma, C. Cui, X. Nie, G. Yang, K. Shaheed, and Y. Yin. 2019. Pre-course student performance prediction with multi-instance multi-label learning. Science China Information Sciences 62, 2, Article 029101 (2019).Google Scholar
- R. B. Pereira, A. Plastino, B. Zadrozny, and L. H. C. Merschmann. 2018. Categorizing feature selection methods for multi-label classification. Artificial Intelligence Review 49, 1 (2018), 57–78. Google ScholarDigital Library
- Y. Prabhu and M. Varma. 2014. FastXML: A fast, accurate and stable tree-classifier for extreme multi-label learning. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 263–272. Google ScholarDigital Library
- G.-J. Qi, X.-S. Hua, Y. Rui, J. Tang, T. Mei, and H.-J. Zhang. 2007. Correlative multi-label video annotation. In Proceedings of the 15th ACM International Conference on Multimedia. 17–26. Google ScholarDigital Library
- J. Read, C. Bielza, and P. Larrañaga. 2014. Multi-dimensional classification with super-classes. IEEE Transactions on Knowledge and Data Engineering 26, 7 (2014), 1720–1733.Google ScholarCross Ref
- J. Read, B. Pfahringer, G. Holmes, and E. Frank. 2011. Classifier chains for multi-label classification. Machine Learning 85, 3 (2011), 333–359. Google ScholarDigital Library
- W. Siblini, P. Kuntz, and F. Meyer. 2018. CRAFTML, an efficient clustering-based random forest for extreme multi-label learning. In Proceedings of the 35th International Conference on Machine Learning. 4664–4673.Google Scholar
- L. Sun, S. Ji, and J. Ye. 2013. Multi-label Dimensionality Reduction. Chapman and Hall/CRC, Boca Ration, FL.Google Scholar
- L. Sun, M. Kudo, and K. Kimura. 2016. Multi-label classification with meta-label-specific features. In Proceedings of the 23rd International Conference on Pattern Recognition. 1612–1617.Google Scholar
- Y.-P. Sun and M.-L. Zhang. 2021. Compositional metric learning for multi-label classification. Frontiers of Computer Science 15, 5, Article 15532 (2021).Google Scholar
- G. Tsoumakas, I. Katakis, and I. Vlahavas. 2011. Random k-labelsets for multi-label classification. IEEE Transactions on Knowledge and Data Engineering 23, 7 (2011), 1079–1089. Google ScholarDigital Library
- L. Wang, Z. Ding, S. Han, J.-J. Han, C. Choi, and Y. Fu. 2019. Generative correlation discovery network for multi-label learning. In Proceedings of the 19th IEEE International Conference on Data Mining. 588–597.Google Scholar
- Y. Wang, W. Zheng, Y. Cheng, and D. Zhao. 2020. Joint label completion and label-specific features for multi-label learning algorithm. Soft Computing 24, 9 (2020), 6553–6569.Google ScholarCross Ref
- W. Weng, Y.-N. Chen, C.-L. Chen, S.-X. Wu, and J.-H. Liu. 2020. Non-sparse label specific features selection for multi-label classification. Neurocomputing 377 (2020), 85–94.Google ScholarDigital Library
- W. Weng, Y. Lin, S. Wu, Y. Li, and Y. Kang. 2018. Multi-label learning based on label-specific features and local pairwise label correlation. Neurocomputing 273 (2018), 385–394. Google ScholarDigital Library
- X. Wu, Q.-G. Chen, Y. Hu, D. Wang, X. Chang, X. Wang, and M.-L. Zhang. 2019. Multi-view multi-label learning with view-specific information extraction. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 3884–3890. Google ScholarDigital Library
- Y. Xing, G. Yu, D. Carlotta, J. Wang, and Z. Zhang. 2018. Multi-label co-training. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2882–2888. Google ScholarDigital Library
- J.-H. Xu, H.-D. Tian, Z.-Y. Wang, Y. Wang, F. Chen, and W.-X. Kang. 2020. Joint input and output space learning for multi-label image classification. IEEE Transactions on Multimedia (2020).Google ScholarCross Ref
- M. Xu and L.-Z. Guo. 2021. Learning from group supervision: The impact of supervision deficiency on multi-label learning. Science China Information Sciences 64, 3, Article 130101 (2021).Google Scholar
- S. Xu, X. Yang, H. Yu, D.-J. Yu, J. Yang, and E. C. C. Tsang. 2016. Multi-label learning with label-specific feature reduction. Knowledge-Based Systems 104 (2016), 52–61. Google ScholarDigital Library
- Y. Yang and S. Gopal. 2012. Multilabel classification with meta-level features in a learning-to-rank framework. Machine Learning 88, 1--2 (2012), 47–68. Google ScholarDigital Library
- C. K. Yeh, W. C. Wu, W. J. Ko, and Y. C. F. Wang. 2017. Learning deep latent spaces for multi-label classification. In Proceedings of the 31st AAAI Conference on Artificial Intelligence. 2838–2844. Google ScholarDigital Library
- C. Zhang, Z. Yu, Q. Hu, P. Zhu, X. Liu, and X. Wang. 2018. Latent semantic aware multi-view multi-label classification. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 4414–4421.Google Scholar
- J. Zhang, C. Li, D. Cao, Y. Lin, S. Su, L. Dai, and S. Li. 2018. Multi-label learning with label-specific features by resolving label correlations. Knowledge-Based Systems 159 (2018), 148–157. Google ScholarCross Ref
- M.-L. Zhang, Y.-K. Li, Y.-Y. Liu, and X. Geng. 2018. Binary relevance for multi-label learning: An overview. Frontiers of Computer Science 12, 2 (2018), 191–202. Google ScholarDigital Library
- M.-L. Zhang and L. Wu. 2015. LIFT: Multi-label learning with label-specific features. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 1 (2015), 107–120.Google ScholarCross Ref
- M.-L. Zhang and Z.-H. Zhou. 2014. A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering 26, 8 (2014), 1819–1837.Google ScholarCross Ref
- Q.-W. Zhang and M.-L. Zhang. 2018. Feature-induced labeling information enrichment for multi-label learning. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 4446–4453.Google Scholar
- Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, and Y.-F. Li. 2012. Multi-instance multi-label learning. Artificial Intelligence 176, 1 (2012), 2291–2320. Google ScholarDigital Library
- S. Zhu, X. Ji, W. Xu, and Y. Gong. 2005. Multi-labelled classification using maximum entropy method. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 274–281. Google ScholarDigital Library
Index Terms
- BiLabel-Specific Features for Multi-Label Classification
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