Page 365 - 《软件学报》2024年第4期
P. 365
吕沈欢 等: 多标记学习中基于交互表示的深度森林方法 1943
251]
[8] Vens C, Struyf J, Schietgat L, Džeroski S, Blockeel H. Decision trees for hierarchical multi-label classification. Machine Learning, 2008,
73(2): 185–214. [doi: 10.1007/s10994-008-5077-3]
[9] Liu SY, Song XH, Ma ZC, Ganaa ED, Shen XJ. MoRE: Multi-output residual embedding for multi-label classification. Pattern
Recognition, 2022, 126: 108584. [doi: 10.1016/j.patcog.2022.108584]
[10] Zhang ML, Zhou ZH. Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans. on
Knowledge and Data Engineering, 2006, 18(10): 1338–1351. [doi: 10.1109/TKDE.2006.162]
[11] Zhou ZH, Feng J. Deep forest. National Science Review, 2019, 6(1): 74–86. [doi: 10.1093/nsr/nwy108]
[12] Lyu SH, Yang L, Zhou ZH. A refined margin distribution analysis for forest representation learning. In: Proc. of the 33rd Int’l Conf. on
Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2019. 5530–5540.
[13] Yang L, Wu XZ, Jiang Y, Zhou ZH. Multi-label learning with deep forest. In: Proc. of the 24th European Conf. on Artificial Intelligence.
Santiago de Compostela: ECAI, 2020. 1634–1641.
[14] Wang QW, Yang L, Li YF. Learning from weak-label data: A deep forest expedition. In: Proc. of the 34th AAAI Conf. on Artificial
Intelligence, 2020, 34(4): 6251–6258. [doi: 10.1609/aaai.v34i04.6092]
[15] Chen YN, Weng W, Wu SX, Chen BH, Fan YL, Liu JH. An efficient stacking model with label selection for multi-label classification.
Applied Intelligence, 2021, 51(1): 308–325. [doi: 10.1007/s10489-020-01807-z]
[16] Ma PF, Wu YX, Li Y, Guo L, Li Z. DBC-Forest: Deep forest with binning confidence screening. Neurocomputing, 2022, 475: 112–122.
[doi: 10.1016/j.neucom.2021.12.075]
[17] Ma PF, Wu YX, Li Y, Guo L, Jiang H, Zhu XQ, Wu XD. HW-Forest: Deep forest with hashing screening and window screening. ACM
Trans. on Knowledge Discovery from Data, 2022, 16(6): 123. [doi: 10.1145/3532193]
[18] Yu QZ, Dong ZH, Fan XY, Zong LC, Li Y. HMD-AMP: Protein language-powered hierarchical multi-label deep forest for annotating
antimicrobial peptides. arXiv:2111.06023, 2021.
[19] Basu S, Kumbier K, Brown J B, Yu B. Iterative random forests to discover predictive and stable high-order interactions. Proc. of the
National Academy of Sciences of the United States of America, 2018, 115(8): 1943–1948. [doi: 10.1073/pnas.1711236115]
[20] Liang SP, Pan WW, You DL, Liu Z, Yin L. Incremental deep forest for multi-label data streams learning. Applied Intelligence, 2022,
52(12): 13398–13414. [doi: 10.1007/s10489-022-03414-6]
[21] Liu WW, Wang HB, Shen XB, Tsang IW. The emerging trends of multi-label learning. IEEE Trans. on Pattern Analysis and Machine
Intelligence, 2022, 44(11): 7955–7974. [doi: 10.1109/TPAMI.2021.3119334]
[22] Behr M, Wang Y, Li X, Yu B. Provable boolean interaction recovery from tree ensemble obtained via random forests. Proc. of the
National Academy of Sciences of the United States of America, 2022, 119(22): e2118636119. [doi: 10.1073/pnas.2118636119]
[23] Chen YH, Lyu SH, Jiang Y. Improving deep forest by exploiting high-order interactions. In: Proc. of the 2021 IEEE Int’l Conf. on Data
Mining. Auckland: IEEE, 2021. 1030—1035. [doi: 10.1109/ICDM51629.2021.00118]
[24] Kocev D, Vens C, Struyf J, Džeroski S. Tree ensembles for predicting structured outputs. Pattern Recognition, 2013, 46(3): 817–833.
[doi: 10.1016/j.patcog.2012.09.023]
[25] Nakano FK, Pliakos K, Vens C. Deep tree-ensembles for multi-output prediction. Pattern Recognition, 2022, 121: 108211. [doi: 10.1016/j.
patcog.2021.108211]
[26] Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504–507. [doi: 10.
1126/science.1127647]
[27] Read J, Reutemann P, Pfahringer B, Holmes G. MEKA: A multi-label/multi-target extension to weka. Journal of Machine Learning
Research, 2016 17(1): 667–671.
[28] Tsoumakas G, Vlahavas IP. Random k-labelsets: An ensemble method for multilabel classification. In: Proc. of the 18th European Conf.
on Machine Learning. Warsaw: ECML, 2007. 406–417.
[29] 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]
[30] Benites F, Sapozhnikova E. HARAM: A hierarchical ARAM neural network for large-scale text classification. In: Proc. of the 2015 IEEE
Int’l Conf. on Data Mining Workshop. Atlantic City: IEEE, 2015. 847–854. [doi: 10.1109/ICDMW.2015.14]