Page 280 - 《软件学报》2026年第2期
P. 280

杭均一 等: 基于不变性注入的多标记类属特征学习                                                         759


                     paper/2021/file/d6539d3b57159babf6a72e106beb45bd-Paper.pdf
                  [5]   Li YX, Ji SW, Kumar S, Ye JP, Zhou ZH. Drosophila gene expression pattern annotation through multi-instance multi-label learning.
                     IEEE/ACM Trans. on Computational Biology and Bioinformatics, 2012, 9(1): 98–112. [doi: 10.1109/TCBB.2011.73]
                  [6]   Guan  QJ,  Huang  YP.  Multi-label  chest  X-ray  image  classification  via  category-wise  residual  attention  learning.  Pattern  Recognition
                     Letters, 2020, 130: 259–266. [doi: 10.1016/j.patrec.2018.10.027]
                  [7]   Chen C, Zhang M, Zhang YF, Ma WZ, Liu YQ, Ma SP. Efficient heterogeneous collaborative filtering without negative sampling for
                     recommendation. In: Proc. of the 34th AAAI Conf. on Artificial Intelligence. New York: AAAI, 2020. 19–26. [doi: 10.1609/aaai.v34i01.
                     5329]
                  [8]   Zhang ML, Wu L. LIFT: Multi-label learning with label-specific features. IEEE Trans. on Pattern Analysis and Machine Intelligence,
                     2015, 37(1): 107–120. [doi: 10.1109/TPAMI.2014.2339815]
                  [9]   Huang J, Li GR, Huang QM, Wu XD. Learning label-specific features and class-dependent labels for multi-label classification. IEEE
                     Trans. on Knowledge and Data Engineering, 2016, 28(12): 3309–3323. [doi: 10.1109/TKDE.2016.2608339]
                 [10]   Jia XY, Zhu SS, Li WW. Joint label-specific features and correlation information for multi-label learning. Journal of Computer Science
                     and Technology, 2020, 35(2): 247–258. [doi: 10.1007/s11390-020-9900-z]
                 [11]   Guo YM, Chung FL, Li GZ, Wang JC, Gee JC. Leveraging label-specific discriminant mapping features for multi-label learning. ACM
                     Trans. on Knowledge Discovery from Data, 2019, 13(2): 24. [doi: 10.1145/3319911]
                 [12]   Tibshirani  R.  Regression  shrinkage  and  selection  via  the  LASSO.  Journal  of  the  Royal  Statistical  Society  Series  B:  Statistical
                     Methodology, 1996, 58(1): 267–288. [doi: 10.1111/j.2517-6161.1996.tb02080.x]
                 [13]   Hang JY, Zhang ML. Dual perspective of label-specific feature learning for multi-label classification. In: Proc. of the 39th Int’l Conf. on
                     Machine Learning. Baltimore: PMLR, 2022. 8375–8386.
                 [14]   Wang YL, Pan XR, Song SJ, Zhang H, Wu C, Huang G. Implicit semantic data augmentation for deep networks. In: Proc. of the 33rd Int’l
                     Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2019. 12635–12644.
                 [15]   Boutell MR, Luo JB, Shen XP, Brown CM. Learning multi-label scene classification. Pattern Recognition, 2004, 37(9): 1757–1771. [doi:
                     10.1016/j.patcog.2004.03.009]
                 [16]   Zhang ML, Zhou ZH. ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition, 2007, 40(7): 2038–2048. [doi: 10.
                     1016/j.patcog.2006.12.019]
                 [17]   Elisseeff  A,  Weston  J.  A  kernel  method  for  multi-labelled  classification.  In:  Proc.  of  the  15th  Int’l  Conf.  on  Neural  Information
                     Processing Systems: Natural and Synthetic. Vancouver: MIT Press, 2001. 681–687.
                 [18]   Zhu  Y,  Kwok  JT,  Zhou  ZH.  Multi-label  learning  with  global  and  local  label  correlation.  IEEE  Trans.  on  Knowledge  and  Data
                     Engineering, 2018, 30(6): 1081–1094. [doi: 10.1109/TKDE.2017.2785795]
                 [19]   Read J, Pfahringer B, Holmes G, Frank E. Classifier chains for multi-label classification. Machine Learning, 2011, 85(3): 333–359. [doi:
                     10.1007/s10994-011-5256-5]
                 [20]   Tsoumakas G, Katakis I, Vlahavas I. Random k-labelsets for multilabel classification. IEEE Trans. on Knowledge and Data Engineering,
                     2011, 23(7): 1079–1089. [doi: 10.1109/TKDE.2010.164]
                 [21]   Lü SH, Chen YH, Jiang Y. Interaction-representation-based deep forest method in multi-label learning. Ruan Jian Xue Bao/Journal of
                     Software, 2024, 35(4): 1934–1944 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6841.htm [doi: 10.13328/j.cnki.
                     jos.006841]
                 [22]   Liu JY, Jia XY. Multi-label classification algorithm based on association rule mining. Ruan Jian Xue Bao/Journal of Software, 2017,
                     28(11): 2865–2878 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5341.htm [doi: 10.13328/j.cnki.jos.005341]
                 [23]   Wang J, Yang Y, Mao JH, Huang ZH, Huang C, Xu W. CNN-RNN: A unified framework for multi-label image classification. In: Proc.
                     of the 2016 IEEE Conf. on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016. 2285–2294. [doi: 10.1109/CVPR.2016.
                     251]
                 [24]   Yazici VO, Gonzalez-Garcia A, Ramisa A, Twardowski B, van de Weijer J. Orderless recurrent models for multi-label classification. In:
                     Proc. of the 2020 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020. 13437–13446. [doi: 10.1109/
                     CVPR42600.2020.01345]
                 [25]   Chen  ZM,  Wei  XS,  Wang  P,  Guo  YW.  Multi-label  image  recognition  with  graph  convolutional  networks.  In:  Proc.  of  the  2019
                     IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019. 5172–5181. [doi: 10.1109/CVPR.2019.00532]
                 [26]   Chen TS, Lin L, Chen RQ, Hui XL, Wu HF. Knowledge-guided multi-label few-shot learning for general image recognition. IEEE Trans.
                     on Pattern Analysis and Machine Intelligence, 2022, 44(3): 1371–1384. [doi: 10.1109/TPAMI.2020.3025814]
                 [27]   Yeh CK, Wu WC, Ko WJ, Wang YCF. Learning deep latent space for multi-label classification. In: Proc. of the 31st AAAI Conf. on
   275   276   277   278   279   280   281   282   283   284   285