Abstract
Multi-label learning (MLL) trains a classification model from multiple labelled datasets, where each training instance is annotated with a set of class labels simultaneously. Following the binary relevance MLL paradigm, a recently effective spirit is to constructing specific features for each label, instead of training over the original feature space. Existing label-specific methods, however, only consider the information from instance distributions, making the reconstructed features poorly discriminative. In this paper, we propose the generation of Label-spEcific feaTures by simultaneously exploring insTance distributions and fEatuRe distributions, and suggest a new method named Letter. Letter reconstructs two subsets of new features from the instance level and feature level, respectively. More concretely, from the instance level, Letter incorporates a sparse constraint, and from the feature level, we cluster the original features to construct new features as an extension. The combination of these two new feature subsets is the final set of label-specific features. Extensive experiments on a total of 14 benchmark datasets verify the competitive performance of Letter against the existing state-of-the-art MLL methods.
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Notes
|⋅| returns the set cardinality.
We employ the Euclidean as the metric in this paper.
References
Boutell MR, Luo J, Shen X, Brown CM (2004) Learning multi-label scene classification. Pattern Recogn 37(9):1757–1771
Cesa-Bianchi N, Re M, Valentini G (2012) Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference. Mach Learn 88(1-2):209–241
Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):1–27
Chen Y, Zhang F, Zuo W (2018) Deep image annotation and classification by fusing multi-modal semantic topics. KSII Trans Internet Inf Syst 12(1):392–412
Cheng W, Hüllermeier E (2009) Combining instance-based learning and logistic regression for multilabel classification. Mach Learn 76(2-3):211–225
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30
Elisseeff A, Weston J (2002) A kernel method for multi-labelled classification. In: Advances in neural information processing systems, pp 681–687
Fürnkranz J, Hüllermeier E, Mencía EL, Brinker K (2008) Multilabel classification via calibrated label ranking. Mach Learn 73(2):133–153
Gong T, Liu B, Chu Q, Yu N (2019) Using multi-label classification to improve object detection. Neurocomputing 370:174–185
Guo Y, Chung F, Li G, Wang J, Gee JC (2019) Leveraging label-specific discriminant mapping features for multi-label learning. ACM Trans Knowl Discov Data 13(2):24
He ZF, Yang M (2019) Sparse and low-rank representation for multi-label classification. Appl Intell 49(5):1708–1723
Huang J, Li G, Huang Q, Wu X (2015) Learning label specific features for multi-label classification. In: IEEE International conference on data mining, pp 181–190
Huang J, Li G, Huang Q, Wu X (2016) Learning label-specific features and class-dependent labels for multi-label classification. IEEE Trans Knowl Data Eng 28(12):3309–3323
Huang J, Li G, Huang Q, Wu X (2017) Joint feature selection and classification for multilabel learning. IEEE Trans Cybern 48(3):876–889
Jabreel M, Moreno A (2019) A deep learning-based approach for multi-label emotion classification in tweets. Appl Sci 9(6): 1123
Li X, Ouyang J, Zhou X (2015) Centroid prior topic model for multi-label classification. Pattern Recogn Lett 62:8–13
Li X, Ouyang J, Zhou X (2015) Supervised topic models for multi-label classification. Neurocomputing 149:811–819
Li X, Ouyang J, Zhou X (2016) Labelset topic model for multi-label document classification. J Intell Inf Syst 46(4):83–97
Li X, Ouyang J, Zhou X, Lu Y, Liu Y (2015) Supervised labeled latent dirichlet allocation for document categorization. Appl Intell 42(3):581–593
Li X, Ouyang J, Zou Y (2018) Supervised topic models with weighted words: multi-label document classification. Front Inf Technol Electron Eng 19(4):513–523
Ma J, Zhang H, Chow TW (2019) Multilabel classification with label-specific features and classifiers: a coarse-and fine-tuned framework. IEEE Trans Cybern:1–15
Read J, Pfahringer B, Holmes G, Frank E (2009) Classifier chains for multi-label classification. In: Machine learning and knowledge discovery in databases, pp 254–269
Read J, Pfahringer B, Holmes G, Frank E (2011) Classifier chains for multi-label classification. Mach Learn 85(3):333
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905
Trohidis K, Tsoumakas G, Kalliris G, Vlahavas I (2008) Multi-label classification of music into emotions. In: International society for music information retrieval, vol 8, pp 325–330
Tsoumakas G, Katakis I, Vlahavas I (2009) Mining multi-label data. In: Data mining and knowledge discovery handbook, pp 667–685
Wang T, Liu L, Liu N, Zhang H, Zhang L, Feng S (2020) A multi-label text classification method via dynamic semantic representation model and deep neural network. Appl Intell:1–13
Wang Y, Rao Y, Zhan X, Chen H, Luo M, Yin J (2016) Sentiment and emotion classification over noisy labels. Knowl-Based Syst 111:207–216
Wei X, Yu Z, Zhang C, Hu Q (2018) Ensemble of label specific features for multi-label classification. In: IEEE International conference on multimedia and expo, pp 1–6
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
Xu S, Yang X, Yu H, Yu DJ, Yang J, Tsang EC (2016) Multi-label learning with label-specific feature reduction. Knowl-Based Syst 104:52–61
Yu Q, Wang J, Zhang S, Gong Y, Zhao J (2017) Combining local and global hypotheses in deep neural network for multi-label image classification. Neurocomputing 235:38–45
Zhang J, Li C, Cao D, Lin Y, Su S, Dai L, Li S (2018) Multi-label learning with label-specific features by resolving label correlations. Knowl-Based Syst 159:148–157
Zhang JJ, Fang M, Li X (2015) Multi-label learning with discriminative features for each label. Neurocomputing 154:305–316
Zhang ML (2011) Lift: Multi-label learning with label-specific features. In: International joint conference on artificial intelligence, pp 1609–1614
Zhang ML, Li YK, Liu XY, Geng X (2018) Binary relevance for multi-label learning: an overview. Front Comput Sci 12(2):191–202
Zhang ML, Wu L (2015) Lift: Multi-label learning with label-specific features. IEEE Trans Pattern Anal Mach Intell 37(11):107–120
Zhang ML, Zhou ZH (2007) Ml-knn: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048
Zhang ML, Zhou ZH (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837
Zhang P, Gao W, Liu G (2018) Feature selection considering weighted relevancy. Appl Intell 48(12):4615–4625
Acknowledgements
This work was supported by the National Natural Science Foundation of China (NSFC) No.51805203, the Science and Technology Development Plan of Jilin Province 20190201023JC, and the Development and Reform Commission of Jilin Province 2019C054-2.
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Guan, Y., Li, W., Zhang, B. et al. Multi-label classification by formulating label-specific features from simultaneous instance level and feature level. Appl Intell 51, 3375–3390 (2021). https://doi.org/10.1007/s10489-020-02008-4
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DOI: https://doi.org/10.1007/s10489-020-02008-4