Page 281 - 《软件学报》2026年第2期

760 软件学报 2026 年第 37 卷第 2 期

Artificial Intelligence. San Francisco: AAAI, 2017. 2838–2844. [doi: 10.1609/aaai.v31i1.10769]
[28] Bai JW, Kong SF, Gomes C. Disentangled variational autoencoder based multi-label classification with covariance-aware multivariate
probit model. In: Proc. of the 29th Int’l Joint Conf. on Artificial Intelligence. Yokohama, 2021. 4313–4321.
[29] Zhan W, Zhang ML. Multi-label learning with label-specific features via clustering ensemble. In: Proc. of the 2017 IEEE Int’l Conf. on
Data Science and Advanced Analytics. Tokyo: IEEE, 2017. 129–136. [doi: 10.1109/DSAA.2017.75]
[30] Zhang CY, Li ZS. Multi-label learning with label-specific features via weighting and label entropy guided clustering ensemble.
Neurocomputing, 2021, 419: 59–69. [doi: 10.1016/j.neucom.2020.07.107]
[31] Zhang JJ, Fang M, Li X. Multi-label learning with discriminative features for each label. Neurocomputing, 2015, 154: 305–316. [doi: 10.
1016/j.neucom.2014.11.062]
[32] Weng W, Lin YJ, Wu SX, Li YW, Kang Y. Multi-label learning based on label-specific features and local pairwise label correlation.
Neurocomputing, 2018, 273: 385–394. [doi: 10.1016/j.neucom.2017.07.044]
[33] Zhang JD, Liu KY, Yang XB, Ju HR, Xu SP. Multi-label learning with relief-based label-specific feature selection. Applied Intelligence,
2023, 53(15): 18517–18530. [doi: 10.1007/s10489-022-04350-1]
[34] Mao JX, Hang JY, Zhang ML. Learning label-specific multiple local metrics for multi-label classification. In: Proc. of the 33rd Int’l Joint
Conf. on Artificial Intelligence. Jeju, 2024. 4742–4750. [doi: 10.24963/ijcai.2024/524]
[35] Mao JX, Wang W, Zhang ML. Label specific multi-semantics metric learning for multi-label classification: Global consideration helps.
In: Proc. of the 32nd Int’l Joint Conf. on Artificial Intelligence. Macao, 2023. 4055–4063. [doi: 10.24963/ijcai.2023/451]
[36] Mehravaran Z, Hamidzadeh J, Monsefi R. Feature selection based on correlation label and B-R belief function (FSCLBF) in multi-label
data. Soft Computing, 2024, 28(2): 1445–1457. [doi: 10.1007/s00500-023-08341-3]
[37] Zhao TN, Zhang YJ, Miao DQ. Granular correlation-based label-specific feature augmentation for multi-label classification. Information
Sciences, 2025, 689: 121473. [doi: 10.1016/j.ins.2024.121473]
[38] Hang JY, Zhang ML, Feng YH, Song XC. End-to-end probabilistic label-specific feature learning for multi-label classification. In: Proc.
of the 36th AAAI Conf. on Artificial Intelligence. AAAI, 2022. 6847–6855. [doi: 10.1609/aaai.v36i6.20641]
[39] Weng W, Chen YN, Chen CL, Wu SX, Liu JH. Non-sparse label specific features selection for multi-label classification.
Neurocomputing, 2020, 377: 85–94. [doi: 10.1016/j.neucom.2019.10.016]
[40] Zou YZ, Hu XG, Li PP, Ge YH. Learning shared and non-redundant label-specific features for partial multi-label classification.
Information Sciences, 2024, 656: 119917. [doi: 10.1016/j.ins.2023.119917]
[41] Huang J, Li GR, Huang QM, Wu XD. Joint feature selection and classification for multilabel learning. IEEE Trans. on Cybernetics, 2018,
48(3): 876–889. [doi: 10.1109/TCYB.2017.2663838]
[42] Tan Y, Sun D, Shi Y, Gao LY, Gao QW, Lu YX. Bi-directional mapping for multi-label learning of label-specific features. Applied
Intelligence, 2022, 52: 8147–8166. [doi: 10.1007/s10489-021-02868-4]
[43] Cheng ZW, Tan ZH. Multi-label learning for label-specific features using correlation information with missing label. Expert Systems with
Applications, 2025, 269: 126491. [doi: 10.1016/j.eswa.2025.126491]
[44] Yu ZB, Zhang ML. Multi-label classification with label-specific feature generation: A wrapped approach. IEEE Trans. on Pattern
Analysis and Machine Intelligence, 2022, 44(9): 5199–5210. [doi: 10.1109/TPAMI.2021.3070215]
[45] Hang JY, Zhang ML. Collaborative learning of label semantics and deep label-specific features for multi-label classification. IEEE Trans.
on Pattern Analysis and Machine Intelligence, 2022, 44(12): 9860–9871. [doi: 10.1109/TPAMI.2021.3136592]
[46] Huang T, Jia B B, Zhang M L. Deep multi-dimensional classification with pairwise dimension-specific features. In: Proc. of the 33rd Int’l
Joint Conf. on Artificial Intelligence. Jeju, 2024. 4183–4191. [doi: 10.24963/ijcai.2024/462]
[47] Zhao DW, Tan Y, Sun D, Gao QW, Lu YX, Zhu D. Multi-label learning of missing labels using label-specific features: An embedded
packaging method. Applied Intelligence, 2024, 54(1): 791–814. [doi: 10.1007/s10489-023-05203-1]
[48] Lowe DG. Object recognition from local scale-invariant features. In: Proc. of the 7th IEEE Int’l Conf. on Computer Vision. Kerkyra:
IEEE, 1999. 1150–1157. [doi: 10.1109/ICCV.1999.790410]
[49] Greenspan H, Belongie S, Goodman R, Perona P, Rakshit S, Anderson CH. Overcomplete steerable pyramid filters and rotation
invariance. In: Proc. of the 1994 IEEE Conf. on Computer Vision and Pattern Recognition. Seattle: IEEE, 1994. 222–228. [doi: 10.1109/
CVPR.1994.323833]
[50] Cubuk ED, Zoph B, Mané D, Vasudevan V, Le QV. AutoAugment: Learning augmentation strategies from data. In: Proc. of the 2019
IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019. 113–123. [doi: 10.1109/CVPR.2019.00020]
[51] Lee S, Cho S, Im S. DRANet: Disentangling representation and adaptation networks for unsupervised cross-domain adaptation. In: Proc.
of the 2021 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021. 15247–15256. [doi: 10.1109/