张建军-华南农业大学数学与信息学院

以下信息为主动公开的研究生招生宣传信息

张建军更新日期: 2024年7月8日

张建军，男，汉族，1992年8月生，群众，博士研究生毕业学历，工学博士学位，讲师，硕导。

个人简介

张建军，华南农业大学数学与信息学院（软件学院）首聘副教授，硕士生导师，华南理工大学博士、博士后，主要从事机器学习、深度网络、不平衡数据流学习、医学图像分析等相关领域的研究，主持广东省自然科学基金面上项目、广东省区域联合项目青年基金、广州市基础与应用基础研究项目。近年来已发表学术论文30余篇，其中包括IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Cybernetics、IEEE Transactions on Industrial Informatics、IEEE Transactions on Medical Imaging 等中科院一区国际顶级期刊论文，ICASSP、SMC、IJCNN 等国际权威会议论文。目前担任IEEE TNNLS、IEEE TCYB、Machine Learning 等国际顶级期刊的审稿人。联系方式：jianjunzhang@scau.edu.cn, jjzhangcs@gmail.com. 学者网主页：https://www.scholat.com/jjzhang81。

工作经历

2024-01至今，华南农业大学，数学与信息学院（软件学院），首聘副教授

2021-01至2024-01，华南理工大学，计算机科学与工程学院，博士后，合作导师：陈俊龙教授（IEEE Fellow，欧洲科学院院士，欧洲科学与艺术院院士）

教育经历

2015-09至2020-12，华南理工大学，计算机科学与工程学院，博士，导师：吴永贤教授（IEEE Senior Member）

2019-10至2020-10，加拿大阿尔伯塔大学，电气与计算机工程学院，联合培养博士，导师：Witold Pedrycz 教授（IEEE Fellow，加拿大皇家科学院院士，波兰科学院院士）

2011-09至2015-06，华南理工大学，计算机科学与工程学院，学士

获奖荣誉

国际会议ICC*CC'22 最佳论文奖

社会、学会及学术兼职

目前担任IEEE TNNLS、IEEE TCYB、Machine Learning 等国际顶级期刊与ICASSP、SMC、IJCNN等国际会议的审稿人

研究领域

主要从事机器学习、深度网络、不平衡数据流学习、医学图像分析、智能电网时序数据学习等相关领域的研究

科研项目

1. 广东省自然科学基金面上项目，面向高维带噪类别不平衡数据的鲁棒分类方法研究，主持

2. 广东省区域联合基金青年基金项目，基于集成学习的噪声不平衡数据分类关键技术研究，主持

3. 广州市基础与应用基础研究项目，基于局部泛化误差模型的人类行为识别基础理论与应用研究，主持

4. 国家自然科学基金面上项目，动态深度与宽度神经网络的泛化误差模型，参与

5. 广州市科技计划项目，基于动态哈希的视频人物追踪研究，参与

发表论文

期刊论文(*表示通讯作者)
[17] S. Liu, X. Ma, S. Deng, Y. Suo, J. Zhang*(张建军), W. W. Y. Ng, "Lightweight multimodal Cycle-Attention Transformer towards cancer diagnosis," Expert Systems with Applications (中科院1区top, IF: 7.5), vol. 255(B), article no. 124616, 2024.

[16] W. W. Y. Ng, Q. Zhang, C. Zhong, and J. Zhang*(张建军), "Improving domain generalization by hybrid domain attention and localized maximum sensitivity," Neural Networks (中科院1区top, IF: 7.8), vol. 171, pp. 320-331, 2024.

[15] J. Zhang(张建军), T. Wang, W. W. Y. Ng, and W. Pedrycz, "KNNENS: A k-nearest neighbor ensemble-based method for incremental learning under data stream with emerging new classes," IEEE Transactions on Neural Networks and Learning Systems (中科院1区top, IF: 10.4), vol. 34, no. 11, 2023.

[14] T. Wang, S. Lu, J. Zhang*(张建军), X. Liu, X. Tian, W. W. Y. Ng, and W. Chen, "SBHA: Sensitive binary hashing autoencoder for image retrieval," IEEE Transactions on Cybernetics (中科院1区top, IF: 11.8), Early Access, 2023.

[13] X. Zhang, C. Zhong, J. Zhang*(张建军), T. Wang, and W. W. Y. Ng, "Robust recurrent neural networks for time series forecasting," Neurocomputing (中科院2区top, IF: 6), vol. 526, pp. 143-157, 2023.

[12] W. W. Y. Ng, S. Xu, J. Zhang*(张建军), X. Tian, T. Rong and S. Kwong, "Hashing-based undersampling ensemble for imbalanced pattern classification problems," IEEE Transactions on Cybernetics (中科院1区top, IF: 11.8), vol. 52, no. 2, pp. 1269-1279, Feb. 2022.

[11] T. Wang, M. Zhang, J. Zhang*(张建军), W. W. Y. Ng, and C. L. P. Chen, "BASS: Broad network based on localized stochastic sensitivity," IEEE Transactions on Neural Networks and Learning Systems (中科院1区top, IF: 10.4), Early Access, 2022.

[10] J. Zhang(张建军), T. Wang, W. W. Y. Ng, and W. Pedrycz, "Ensembling perturbation-based oversamplers for imbalanced datasets," Neurocomputing (中科院2区top, IF: 6), vol. 479, pp. 1-11, 2022.

[9] J. Zhang(张建军), T. Wang, W. W. Y. Ng, and W. Pedrycz, "Perturbation-based oversampling technique for imbalanced classification problems," International Journal of Machine Learning and Cybernetics (中科院3区, IF: 5.6), vol. 14, no. 3, pp. 1-15, 2022.

[8] W. W. Y. Ng, Z. Liu, J. Zhang*(张建军), and W. Pedrycz, "Maximizing minority accuracy for imbalanced pattern classification problems using cost-sensitive localized generalization error model," Applied Soft Computing (中科院2区top, IF: 8.7), vol. 104, no. 5, article no. 107178, 2021.

[7] C. S. Lai, Y. Yang, K. Pan, J. Zhang(张建军), H. L. Yuan, W. W. Y. Ng, Y. Gao, Z. Zhao, T. Wang, M. Shahidehpour, and L. L. Lai, "Multi-view neural network ensemble for short and mid-term load forecasting," IEEE Transactions on Power Systems (中科院1区top, IF: 6.6), vol. 36, no. 4, pp. 2992-3003, 2021.

[6] W. W. Y. Ng, Y. Tuo, J. Zhang*(张建军), and S. Kwong, "Training error and sensitivity-based ensemble feature selection," International Journal of Machine Learning and Cybernetics (中科院3区, IF: 5.6), vol. 11, pp. 2313-2326, 2020.

[5] J. Zhang(张建军), X. Chen, W. W. Y. Ng, C. S. Lai, and L. L. Lai, "New appliance detection for nonintrusive load monitoring," IEEE Transactions on Industrial Informatics (中科院1区top, IF: 12.3), vol. 15, no. 8, pp. 4819-4829, Aug. 2019.

[4] W. W. Y. Ng, J. Zhang(张建军), C. S. Lai, W. Pedrycz, L. L. Lai, and X. Wang, "Cost-sensitive weighting and imbalance-reversed bagging for streaming imbalanced and concept drifting in electricity pricing classification," IEEE Transactions on Industrial Informatics (中科院1区top, IF: 12.3), vol. 15, no. 3, pp. 1588-1597, March 2019.

[3] W. W. Y. Ng, Y. Zhang, J. Zhang*(张建军), D. D. Wang, and F. L. Wang, "Stochastic sensitivity tree boosting for imbalanced prediction problems of protein-ligand interaction sites," IEEE Transactions on Emerging Topics in Computational Intelligence (中科院2区, IF: 5.3), 2019.

[2] S. Zhang, W. W. Y. Ng, J. Zhang(张建军), C. D. Nugent, N. Irvine, and T. Wang, "Evaluation of radial basis function neural network minimizing L-GEM for sensor-based activity recognition," Journal of Ambient Intelligence and Humanized Computing (中科院3区), vol. 14, pp. 53-63, 2019.

[1] W. W. Y. Ng, G. Zeng, J. Zhang*(张建军), D. S. Yeung, and W. Pedrycz, "Dual autoencoders features for imbalance classification problem," Pattern Recognition (中科院1区Top，IF: 8), vol. 60, no. 1, pp. 875-889, 2016.

会议论文
[19] X. Yang, S. Deng, S. Liu, Y. Suo, W. W. Y. Ng, J. Zhang*(张建军), "A Mathematics Framework of Artificial Shifted Population Risk and Its Further Understanding Related to Consistency Regularization," ECML PKDD (CCF-B类会议), accepted, 2024.

[18] X. Zhang, C. S. Lai, W. W. Y. Ng, S. Xu, X. Wu, J. Zhang(张建军), K. Pan, T. Wang, and Z. Zhao, "A Probabilistic Solar Irradiance Interval-Valued Prediction Model with Multi-Objective Optimization of Reliability, Sharpness and Stability," 2023 13th International Conference on Information Science and Technology (ICIST, EI会议), pp. 80-87, 2023.

[17] W. W. Y. Ng, P. Zheng, T. Wang, J. Zhang*(张建军), Y. Liang, H. Zhou, D. Liang, G. Li, and X. Wei, "LSSED: A robust segmentation network for inflamed appendix from CT images," 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP, CCF-B类会议), Accepted, 2023.

[16] T. Wang, J. Zhang*(张建军), W. Ng, X. Zhang, S. Zhang, C. Nugent, N. Irvine and S. Kwong, "Feature learning based on stacked adversarial autoencoders for time series change point detection," 2023 IEEE 22nd International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC，EI会议), Accepted, 2023.

[15] T. Wang, W. W. Y. Ng, M. Zhang, X. Zhang, J. Zhang(张建军), M. Deng, "Moisture content prediction of sugi wood drying using deep lstm ae minimizing perturbed error," 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC, CCF-C类会议), Accepted, 2023.

[14] S. Deng, Y. Suo, S. Liu, X. Ma, H. Chen, X. Liao, J. Zhang(张建军), and W. W. Y. Ng, " MFCSA-CAT: A multimodal fusion method for cancer survival analysis based on cross-attention transformer," 2022 5th International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022, EI会议), 2022.

[13] J. Zhang(张建军), T. Wang, W. W. Y. Ng, W. Pedrycz, S. Zhang and C. D. Nugent, "Minority oversampling using sensitivity," 2020 International Joint Conference on Neural Networks (IJCNN, CCF-C类会议), 2020, pp. 1-7.

[12] J. Zhang(张建军), T. Wang, W. W. Y. Ng, and S. Kwong, "Stochastic sensitivity regularized autoencoder for robust feature learning," 2022 IEEE 21st International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC, EI会议，最佳论文奖), 2022.

[11] X. Tian, L. Qiu, Q. Li, W. W. Y. Ng, J. Zhang(张建军), S. Kwong, H. Wang, X. Dong, B. Liu, Y. Hu and H. Yu, "Hashing-based undersampling for large scale histopathology image classification," 2022 IEEE 21st International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC, EI会议), 2022.

[10] J. Zhang(张建军), T. Wang, W. W. Y. Ng, S. Zhang, and C. D. Nugent, "Undersampling near decision boundary for imbalance problems," 2019 International Conference on Machine Learning and Cybernetics (ICMLC, EI会议), 2019.

[9] J. Zhang(张建军), and W. W. Y. Ng, " Stochastic sensitivity measure-based noise filtering and oversampling method for imbalanced classification problems," 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC, CCF-C类会议), 2018.

[8] Z. Liu, J. Zhang(张建军), and W. W. Y. Ng, "Imbalanced high-frequency number classification based on DSUS," 2018 International Conference on Machine Learning and Cybernetics (ICMLC, EI 会议), 2018.

[7] Y. Chen, J. Zhang(张建军), and W. W. Y. Ng, "Loan default prediction using diversified sensitivity undersampling," 2018 International Conference on Machine Learning and Cybernetics (ICMLC, EI 会议), 2018.

[6] S. Zhang, W. W. Y. Ng, J. Zhang(张建军), and C. D. Nugent, "Human activity recognition using radial basis function neural network trained via a minimization of localized generalization error," International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI, EI会议), 2017.

[5] W. W. Y. Ng, Y. Zhang, J. Zhang*(张建军), "Bsmboost for imbalanced pattern classification problems," 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC, CCF-C类会议), 2017.

[4] Y. Chai, J. Zhang(张建军), W. W. Y. Ng, "Weighted ensemble of diversified sensitivity-based undersampling for imbalanced pattern classification problems," 2017 International Conference on Machine Learning and Cybernetics (ICMLC, EI会议), 2017.

[3] W. W. Y. Ng, J. Li, J. Zhang(张建军), Q. Wu, J. Li, "Visual words selection for human action recognition using rbfnn via the minimization of L-GEM," 2017 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR, EI会议), 2017.

[2] T. Chen, T. Wang, W. W. Y. Ng, J. Zhang(张建军), "Feature weighting for rbfnn based on genetic algorithm and localized generalization error model," 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2017.

[1] J. Liao, J. Zhang(张建军), W. W. Y. Ng, "Effects of different base classifiers to Learn++ family algorithms for concept drifting and imbalanced pattern classification problems," 2016 International Conference on Machine Learning and Cybernetics (ICMLC, EI会议), 2016.

科研创新

授权专利
1．吴永贤，刘政锡，张建军，基于代价局部泛化误差的不平衡问题的分类方法，2022-3-29，中国，ZL201910267769.2

2．吴永贤，丘林，田星，张建军，王婷，余洪华，一种针对组织病理学图像的哈希样本平衡癌症标注方法，2023-12-19，中国，ZL202110228166.9