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Learning label-specific features with global and local label correlation for multi-label classification

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Abstract

Multi-label algorithms often use an identical feature space to build classification models for all labels. However, labels generally express different semantic information and should have their own characteristics. A few algorithms have been proposed to find label-specific features to construct discriminative classification models. Some use global label correlation to make the reconstructed features more discriminative, but they usually neglect the local correlation between labels. To solve this problem, we propose a new algorithm, named learning Label-specific Features with Global and Local label Correlation (LFGLC). The algorithm integrates both global and local label correlation to extract label-specific features for each label. Specifically, global label correlation is calculated by the label co-occurrence frequency between label pairs, and local label correlation is learned from the neighborhood of each instance. Comprehensive experiments on 12 multi-label data sets clearly manifest that the proposed algorithm performs competitively in feature selection and multi-label classification.

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Notes

  1. http://mulan.sourceforge.net/datasets.html

  2. http://lamda.nju.edu.cn

  3. http://meka.sourceforge.net

  4. source code: http://cse.seu.edu.cn/PersonalPage/zhangml/index.html

  5. source code: http://www.escience.cn/people/huangjun/index.html

References

  1. Lin J, Cai Q, Lin M (2021) Multi-label classification of Fundus images with graph convolutional network and self-supervised learning. IEEE Signal Process Lett 28:454–458

    Article  Google Scholar 

  2. Huang X, Chen B, Xiao L, Yu J, Jing L (2021) Label-aware document representation via hybrid attention for extreme multi-label text classification. Neural Process Lett, pp 1–17

  3. Wen J, Wei L, Zhou W, Han J, Guo T (2020) GCN-IA: user profile based on graph convolutional network with implicit association labels. In: Conference on computational science. pp 355–364

  4. Sun Z, Zhang J, Dai L, Li C, Zhou C, Xin J, Li S (2019) Mutual information based multi-label feature selection via constrained convex optimization. Neurocomputing 329:447–456

    Article  Google Scholar 

  5. Bayati H, Dowlatshahi M, Paniri M (2020) MLPSO: a filter multi-label feature selection based on particle swarm optimization. In: Conference on computer society of Iran pp 1–6

  6. Zhang J, Luo Z, Li C, Zhou C, Li S (2019) Manifold regularized discriminative feature selection for multi-label learning. Pattern Recognit 95:136–150

    Article  Google Scholar 

  7. Gonzalez-Lopez J, Ventura S, Cano A (2020) Distributed multi-label feature selection using individual mutual information measures. Knowl-Based Syst, p 188

  8. Gonzalez-Lopez J, Ventura S, Cano A (2020) Distributed selection of continuous features in multilabel classification using mutual information. IEEE Trans on Neural Netw Learn Syst 31(7):2280–2293

    MathSciNet  Google Scholar 

  9. Alalga A, Benabdeslem K, Taleb N (2015) Soft-constrained Laplacian score for semi-supervised multi-label feature selection. Knowl Inf Syst 47(1):75–98

    Article  Google Scholar 

  10. Huang R, Jiang W, Sun G (2018) Manifold-based constraint Laplacian score for multi-label feature selection. Pattern Recognit Lett 112:346–352

    Article  Google Scholar 

  11. Zhang ML, Wu L (2015) Lift: multi-label learning with label-specific features. IEEE Trans Pattern Anal Mach Intell 37(1):107–120

    Article  Google Scholar 

  12. Guan Y, Li W, Zhang B, Han B, Ji M (2020) Multi-label classification by formulating label-specific features from simultaneous instance level and feature level. Applied Intell 9:1–16

    Google Scholar 

  13. Guo Y, Chung F, Li G, Wang J, Gee JC (2019) leveraging Label-specific discriminant mapping features for multi-label learning. ACM Trans Knowl Discovery Data 13(2):1–23

    Article  Google Scholar 

  14. Weng W, Lin Y, Wu S, Li Y, Kang Y (2018) Multi-label learning based on label specific features and local pairwise label correlation. Neurocomputing 273:385–394

    Article  Google Scholar 

  15. Huang J, Li GR, Wang SH, Xue Z, Huang QM (2017) Multi-Label Classification by exploiting local positive and negative pairwise label correlation. Neurocomputing 257:164–174

    Article  Google Scholar 

  16. Huang R, Kang L (2021) Local positive and negative label correlation analysis with label awareness for multi-label classification. Int J Mach Learn Cybern, pp 1–14

  17. Cheng Z, Zeng Z (2020) Joint label-specific features and label correlation for multi-label learning with missing label. Applied Intell 50(11):4029–4049

    Article  Google Scholar 

  18. Bao J, Wang Y, Cheng Y (2021) Asymmetry label correlation for multi-label learning. Applied Intell, pp 1–13

  19. Che X, Chen D, Mi J (2021) Feature distribution-based label correlation in multi-label classification. Int J Mach Learn Cybern, pp 1–15

  20. Read J, Pfahringer B, Holmes G, Frank E (2011) Classifier chains for multi-label classification. Mach Learn 85:333–359

    Article  MathSciNet  Google Scholar 

  21. Che X, Chen D, Mi J (2020) A novel approach for learning label correlation with application to feature selection of multi-label data. Inf Sci 512:795–812

    Article  MathSciNet  MATH  Google Scholar 

  22. Li Q, Peng X, Qiao Y, Peng Q (2020) Learning label correlations for multi-label image recognition with graph networks. Pattern Recognit Lett 138:378–384

    Article  Google Scholar 

  23. Ma J, Chiu B, Chow T (2020) Multilabel classification with group-based mapping: a framework with local feature selection and local label correlation. IEEE Trans Cybern

  24. Nan G, Li Q, Dou R, Liu J (2018) Local positive and negative correlation-based k-labelsets for multi-label classification. Neurocomputing 318:90–101

    Article  Google Scholar 

  25. Xiao J, Tang S (2020) Joint Learning of Binary Classifiers and Pairwise Label Correlations for Multi-label Image Classification. In: IEEE conference on multimedia information processing and retrieval. pp 25–30

  26. Li YK, Zhang ML, Geng X (2015) Leveraging implicit relative labeling-importance information for effective multi-label learning. In: IEEE international conference on data mining. pp 251–260

  27. Boutell MR, Luo J, Shen X, Brown CM (2014) Learning multi-label scene classification. Pattern Recognit 37:1757–1771

    Article  Google Scholar 

  28. Zhang ML, Zhou ZH (2007) Ml-knn: a lazy learning approach to multi-label learning. Pattern Recognit 40:2038–2048

    Article  MATH  Google Scholar 

  29. Wu G, Tian Y, Liu D (2018) Cost-sensitive multi-label learning with positive and negative label pairwise correlations. Neural Netw 108:411–423

    Article  Google Scholar 

  30. Xu H, Xu L (2017) Multi-label feature selection algorithm based on label pairwise ranking comparison transformation. In: International joint conference on neural networks. pp 1210–1217

  31. Zhang Y, Zhao T, Miao D, Pedrycz W (2021) Granular multilabel batch active learning with pairwise label correlation. IEEE Trans on Systems, Man, and Cybern

  32. Wang R, Ye S, Li K, Kwong S (2021) Bayesian network based label correlation analysis for multi-label classifier chain. Inf Sci 554:256–275

    Article  MathSciNet  MATH  Google Scholar 

  33. He Z F, Yang M, Gao Y, Liu H D, Yin Y (2019) Joint multi-label classification and label correlations with missing labels and feature selection. Knowl-Based Syst 163:145–158

    Article  Google Scholar 

  34. Zhu Y, Kwok J T, Zhou Z H (2018) Multi-Label Learning with global and local label correlation. IEEE Trans Knowl Data Eng 30(6):1081–1094

    Article  Google Scholar 

  35. Yan Y, Li S, Xiao Z, Wang A, Li Z, Zhang J (2018) k-Labelsets for Multimedia Classification with Global and Local Label Correlation. In: International conference on multimedia Mmodeling. pp 177–188

  36. Huang J, Li GR, Huang QM, Wu XD (2015) Learning label specific features for multi-label classification. In: IEEE international conference on data mining. pp 181–190

  37. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surveys 31(3):264–323

    Article  Google Scholar 

  38. Wei XY, Yu ZW, Zhang CQ, Hu QH (2018) Ensemble of label specific features for multi-label classification. In: IEEE international conference on multimedia and expo. pp 1–6

  39. Huang J, Xu L, Qian K, Wang J, Yamanishi K (2021) Multi-label learning with missing and completely unobserved labels. IEEE Trans Knowl Data Eng 35:1061–1086

    MathSciNet  MATH  Google Scholar 

  40. Weng W, Chen YN, Chen CL, Wu SX, Liu JH (2020) Non-sparse label specific features selection for multi-label classification. Neurocomputing 377:85–94

    Article  Google Scholar 

  41. Huang J, Li GR, Huang QM, Wu XD (2018) Joint feature selection and classification for multilabel learning. IEEE Trans Cybern 48(3):876–889

    Article  Google Scholar 

  42. Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. Siam J Imaging Sci 2(1):183–202

    Article  MathSciNet  MATH  Google Scholar 

  43. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc 58(1):267–288

    MathSciNet  MATH  Google Scholar 

  44. Chang CC, Lin CJ (2011) LIBSVM: A library for support vectormachines. ACM Trans Intell Syst Technol 2(3):1–27

    Article  Google Scholar 

  45. Demiar J, Schuurmans D (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(1):1–30

    MathSciNet  Google Scholar 

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Acknowledgements

This research was supported by the natural Science Foundation of Fujian Province of China (No. 2021J011187).

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Authors and Affiliations

  1. Department of Computer and Information Engineering, Xiamen University of Technology, Xiamen, 361024, China

    Wei Weng, Bowen Wei & Wen Ke

  2. School of Automation, Xiamen University, Xiamen, 361005, China

    Yuling Fan

  3. School of Economics, Xiamen University, Xiamen, 361005, China

    Jinbo Wang

  4. School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China

    Yuwen Li

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  1. Wei Weng
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  2. Bowen Wei
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  3. Wen Ke
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  4. Yuling Fan
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  5. Jinbo Wang
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  6. Yuwen Li
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Correspondence to Wei Weng.

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Wei Weng and Bowen Wei contributed equally to this work.

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Weng, W., Wei, B., Ke, W. et al. Learning label-specific features with global and local label correlation for multi-label classification. Appl Intell 53, 3017–3033 (2023). https://doi.org/10.1007/s10489-022-03386-7

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