Học các đặc trưng riêng biệt cho nhãn thông qua mạng nơ-ron cho phân loại nhiều nhãn

Gargiulo F, Silvestri S, Ciampi M, De Pietro G (2019) Deep neural network for hierarchical extreme multi-label text classification. Appl Soft Comput 79:125–138 Li Y, Song Y, Luo J (2017) Improving pairwise ranking for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3617–3625 Wen S, Liu W, Yang Y, Zhou P, Guo Z, Yan Z, Chen Y, Huang T (2020) Multilabel image classification via feature/label co-projection. IEEE Trans Syst Man Cybern Syst 51:7250–7259 Gull S, Shamim N, Minhas F (2019) Amap: hierarchical multi-label prediction of biologically active and antimicrobial peptides. Comput Biol Med 107:172–181 Liu L, Tang L, Jin X, Zhou W (2019) A multi-label supervised topic model conditioned on arbitrary features for gene function prediction. Genes 10(1):57 Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837 Boutell MR, Luo J, Shen X, Brown CM (2004) Learning multi-label scene classification. Pattern Recogn 37(9):1757–1771 Zhang M-L, Zhou Z-H (2007) Ml-knn: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048 Gong C, Tao D, Yang J, Liu W (2016) Teaching-to-learn and learning-to-teach for multi-label propagation. In: Proceedings of the AAAI conference on artificial intelligence, vol 30 Weng W, Lin Y, Shunxiang W, Li Y, Kang Y (2018) Multi-label learning based on label-specific features and local pairwise label correlation. Neurocomputing 273:385–394 Read J, Pfahringer B, Holmes G, Frank E (2011) Classifier chains for multi-label classification. Mach Learn 85(3):333–359 Zhao W, Kong S, Bai J, Fink D, Gomes C (2021) Learning high-order label correlation for multi-label classification via attention-based variational autoencoders. arXiv preprint arXiv:2103.06375 Guo B, Hou C, Nie F, Yi D (2016) Pervised multi-label dimensionality reduction. In: 2016 IEEE 16th international conference on data mining (ICDM. IEEE), pp 919–924 Øyvind MK, Cristina S-R, Maria BF, Robert J (2019) Noisy multi-label semi-supervised dimensionality reduction. Pattern Recogn 90:257–270 Zhang M-L, Lei W (2014) Lift: multi-label learning with label-specific features. IEEE Trans Pattern Anal Mach Intell 37(1):107–120 Huang J, Li G, Huang Q, Wu X (2015) Learning label specific features for multi-label classification. In: 2015 IEEE international conference on data mining. IEEE, pp 181–190 Huang J, Li G, Huang Q, Xindong W (2017) Joint feature selection and classification for multilabel learning. IEEE Trans Cybern 48(3):876–889 Zhang M-L, Zhou Z-H (2006) Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans Knowl Data Eng 18(10):1338–1351 Bello M, Nápoles G, Sánchez R, Bello R, Vanhoof K (2020) Deep neural network to extract high-level features and labels in multi-label classification problems. Neurocomputing 413:259–270 Nam J, Kim J, Mencía EL, Gurevych I, Furnkranz J (2014) Large-scale multi-label text classification–revisiting neural networks. Joint European conference on machine learning and knowledge discovery in databases. Springer, Berlin, pp 437–452 Weizhi Liao Yu, Wang YY, Zhang X, Ma P (2020) Improved sequence generation model for multi-label classification via cnn and initialized fully connection. Neurocomputing 382:188–195 Wang J, Yang Y, Mao J, Huang Z, Huang C, Xu W (2016) Cnn-rnn: a unified framework for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2285–2294 Zhang M-L (2009) M l-rbf: Rbf neural networks for multi-label learning. Neural Process Lett 29(2):61–74 Huimin L, Zhang M, Xing X, Li Y, Shen HT (2020) Deep fuzzy hashing network for efficient image retrieval. IEEE Trans Fuzzy Syst 29(1):166–176 Xie Y, Zhang J, Xia Y, Shen C (2020) A mutual bootstrapping model for automated skin lesion segmentation and classification. IEEE Trans Med Imaging 39(7):2482–2493 Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507 Cheng Y, Zhao D, Wang Y, Pei G (2019) Multi-label learning with kernel extreme learning machine autoencoder. Knowl-Based Syst 178:1–10 Parwez MA, Abulaish M et al (2019) Multi-label classification of microblogging texts using convolution neural network. IEEE Access 7:68678–68691 Zhu J, Liao S, Lei Z, Li SZ (2017) Multi-label convolutional neural network based pedestrian attribute classification. Image Vis Comput 58:224–229 Nam J, Mencía EL, Kim HJ, Fürnkranz J (2017) Maximizing subset accuracy with recurrent neural networks in multi-label classification. In: Proceedings of the 31st international conference on neural information processing systems, pp 5419–5429 Chen SF, Chen YC, Yeh CK, Wang YCF (2018) Order-free rnn with visual attention for multi-label classification. In: Thirty-Second AAAI conference on artificial intelligence Rui H, Liuyue K (2021) Local positive and negative label correlation analysis with label awareness for multi-label classification. Int J Mach Learn Cybern 12:1–14 Bidgoli AA, Ebrahimpour-komleh H, Rahnamayan S (2021) A novel binary many-objective feature selection algorithm for multi-label data classification. Int J Mach Learn Cybern 12(7):2041–2057 Huang J, Qin F, Zheng X, Cheng Z, Yuan Z, Zhang W, Huang Q (2019) Improving multi-label classification with missing labels by learning label-specific features. Inf Sci 492:124–146 Zhu W, Li W, Jia X (2020) Multi-label learning with local similarity of samples. In: 2020 International joint conference on neural networks (IJCNN). IEEE, pp 1–8 Zhu Y, Kwok JT, Zhou Z-H (2017) Multi-label learning with global and local label correlation. IEEE Trans Knowl Data Eng 30(6):1081–1094 Jie B, Zhang D, Cheng B, Shen D, Initiative ADN (2015) Manifold regularized multitask feature learning for multimodality disease classification. Hum Brain Mapp 36(2):489–507 Han H, Mengxing Huang Yu, Zhang XY, Feng W (2019) Multi-label learning with label specific features using correlation information. IEEE Access 7:11474–11484 Gersho A, Gray RM (2012) Vector quantization and signal compression, vol 159. Springer, Berlin Abdel-Ghaffar KAS (2019) Sets of binary sequences with small total hamming distances. Inf Process Lett 142:27–29 Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imag Sci 2(1):183–202 Lin Z, Ganesh A, Wright J, Wu L, Chen M, Ma Y (2009) Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix. Coordinated Science Laboratory Report no. UILU-ENG-09-2214, DC-246 Wang Y, Zheng W, Cheng Y, Zhao D (2020) Joint label completion and label-specific features for multi-label learning algorithm. Soft Comput 24(9):6553–6569 Tan Y, Sun D, Shi Y, Gao L, Gao Q, Lu Y (2021) Bi-directional mapping for multi-label learning of label-specific features. Appl Intell 52:1–20