[1] Ping Zhang;Guixia Liu;Jiazhi Song. MFSJMI: Multi-label feature selection considering join mutual information and interaction weight. Pattern Recognition, 2023, 138 , 109378.

[2] Ping Zhang, Liu GX, Gao WF, et al. Multi-label feature selection considering label supplementation. Pattern Recognition, 2021, 120, 108137.

[3] Ping Zhang, Gao WF, Hu JC, et al.. Multi-label feature selection based on the division of label topics. Information Sciences, 2021, 553(10), 129-153.

[4] Ping Zhang, Liu GX, Gao WF.. Distinguishing two types of labels for multi-label feature selection. Pattern Recognition, 2019, 95, 72-82.

[5] Wanfu Gao^1,2;1;Pingting Hao^1,2;2;Yang Wu^1,2;3;Ping Zhang^1,3;CA4. A unified low-order information-theoretic feature selection framework for multi-label learning. Pattern Recognition, 2022, , 109111.

[6] Gao W, Hu L, Ping Zhang*. Feature redundancy term variation for mutual information-based feature selection. Applied Intelligence, 2020, 50, 1272-1288.

[7] Gao W, Hu L, Li Y, Ping Zhang*. Preserving similarity and staring decisis for feature selection. IEEE Transactions on Artificial Intelligence, 2021, 2(6), 584-593.

[8] Ping Zhang, Sheng J, Gao W. Multi-Label Feature Selection Method Based on Dynamic Weight. Soft Computing, 2022, 26(6), 2793-2805.

[9] Ping Zhang, Gao WF.. Feature relevance term variation for multi-label feature selection. Applied Intelligence, 2021, 51, 5095-5110.

[10] Ping Zhang, Gao WF, Liu GX.. Feature selection considering weighted relevancy. Applied Intelligence, 2018, 48(12), 4615-4625.