张忠良

部分已发表期刊论文

[1]  Zhong-Liang Zhang, Yun-Hao Zhu, Xing-Gang Luo. DES-AS: Dynamic ensemble selection based on algorithm Shapley[J]. Pattern Recognition, 2025, 157: 110899.（中科院一区）

[2]  Qin-Nan Cai (本科生), Zhong-Liang Zhang, Yu-Heng Wu, et al. LSMOTE: A link-based Synthetic Minority Oversampling Technique for binary imbalanced datasets[J]. Neurocomputing, 2024, 608: 128372.（中科院二区）

[3]  Jian Pei (本科生), Zhong-Liang Zhang (通讯作者), Wan-An Liu. Sentiment classification of movie reviews: a powerful method based on ensemble of classifiers and features[J]. International Journal of Machine Learning and Cybernetics, 2024, 15(12): 6027-6048. （中科院三区）

[4]  Ning Wang (本科生), Zhong-Liang Zhang (通讯作者), Xing-Gang Luo. Iterative minority oversampling and its ensemble for ordinal imbalanced datasets. Engineering Applications of Artificial Intelligence, 2024, 127: 107211.（中科院一区）

[5]  Jiang-Qi Jiang, Xing-Gang Luo, Wei Jiang, Zhong-Liang Zhang. IPTV advertising scheduling methods considering advertising repetition effects[J]. IEEE Transactions on Engineering Management, 2023, 71: 1585-1597.（中科院三区）

[6]  Hong-Jie Li (博士生), Xing-Gang Luo, Zhong-Liang Zhang, et al. Driving risk prevention in usage-based insurance services based on interpretable machine learning and telematics data[J]. Decision Support Systems, 2023, 172: 113985.（中科院一区）

[7]  Xing-Gang Luo, Yu-Qing Du, Qiu-Yan Chen, Zhong-Liang Zhang, et al. Joint optimization of low-carbon product family configuration and smart production line selection. Computers & Industrial Engineering, 2023: 109403.（中科院一区）

[8]  Ke Li (博士生), Chen-Yong Chen, Zhong-Liang Zhang (通讯作者). Mining online reviews for ranking products: A novel method based on multiple classifiers and interval-valued intuitionistic fuzzy TOPSIS. Applied Soft Computing, 2023, 139: 110237.（中科院一区）

[9]  Zhong-Liang Zhang, Rui-Rui Peng, Yuan-Peng Ruan, et al. ESMOTE: an overproduce-and-choose synthetic examples generation strategy based on evolutionary computation. Neural Computing and Applications, 2023, 35(9): 6891-6977.（中科院三区）

[10]Zhong-Liang Zhang, Chen-Yue Zhang, Xing-Gang Luo, et al. A multiple classifiers system with roulette-based feature subspace selection for one-vs-one scheme. Pattern Analysis and Applications, 2023, 26(1): 73-90.（中科院四区）

[11]Xing-Gang Luo, Zhong-Liang Zhang, Jia-Huan He, et al. Strategic analysis of the parameter servers and participants in federated learning: an evolutionary game perspective[J]. IEEE Transactions on Computational Social Systems, 2022, 11(1): 132-143.（中科院二区）

[12]Xing-Gang Luo, Xin-Rui Liu, Peng-Li Ji, Zhong-Liang Zhang (通讯作者), et al. Trip planning for visitors in a service system with capacity constraints. Computers & Operations Research, 2022, 148: 105974.（中科院二区）

[13]Xing-Gang Luo, Lun Liu, Xuan-Zhu Shang, Zhong-Liang Zhang (通讯作者). Service configuration optimization for multiple customers considering service process information and time-varying service resources[J]. Computers & Industrial Engineering, 2022, 173: 108710.（中科院一区）

[14]Qiong Chen (本科生), Zhong-Liang Zhang (通讯作者), Wen-Po Huang, et al. PF-SMOTE: A novel parameter-free SMOTE for imbalanced datasets. Neurocomputing, 2022, 498: 75-88.（中科院二区）

[15]Zhong-Liang Zhang,Xing-Gang Luo, Qing Zhou. DRCW-FRkNN-OVO: distance-based related competence weighting based on fixed radius k nearest neighbour for one-vs-one scheme. International Journal of Machine Learning and Cybernetics, 2022, 13: 1441-1459.（中科院三区）

[16]Bo-Wen Yuan (博士生), Zhong-Liang Zhang, Xing-Gang Luo, et al. OIS-RF: a novel overlap and imbalance sensitive random forest. Engineering Applications of Artificial Intelligence, 2021, 104: 104355.（中科院一区）

[17]Bo-Wen Yuan (博士生), Xing-Gang Luo, Zhong-Liang Zhang, et al. A novel density-based adaptive k nearest neighbor method for dealing with overlapping problem in imbalanced datasets. Neural Computing and Applications, 2021, 33: 4457-4481.（中科院三区）

[18]Zhong-Liang Zhang, Yu-Yu Chen, Jing Li, et al. A distance-based weighting framework for boosting the performance of dynamic ensemble selection[J]. Information Processing & Management, 2019, 56(4): 1300-1316.（中科院一区）

[19]Zhong-Liang Zhang, Xing-Gang Luo, Yang Yu, et al. Integration of an improved dynamic ensemble selection approach to enhance one-vs-one scheme[J]. Engineering Applications of Artificial Intelligence, 2018, 74: 43-53.（中科院一区）

[20]Salvador García, Zhong-Liang Zhang, Abdulrahman Altalhi, et al. Dynamic ensemble selection for multi-class imbalanced datasets[J]. Information Sciences, 2018, 445: 22-37.（中科院一区）

[21]Zhong-Liang Zhang, Xing-Gang Luo, Salvador García, et al. DRCW-ASEG: One-versus-one distance-based relative competence weighting with adaptive synthetic example generation for multi-class imbalanced datasets[J]. Neurocomputing, 2018, 285: 176-187.（中科院二区）

[22]Zhong-Liang Zhang, Xing-Gang Luo, Salvador García, et al. Cost-sensitive back-propagation neural networks with binarization techniques in addressing multi-class problems and non-competent classifiers[J]. Applied Soft Computing, 2017, 56: 357-367.（中科院一区）

[23]Zhong-Liang Zhang, Xing-Gang Luo, Salvador García, et al. Exploring the effectiveness of dynamic ensemble selection in the one-versus-one scheme[J]. Knowledge-Based Systems, 2017, 125: 53-63.（中科院一区）

[24]Zhong-Liang Zhang, Bartosz Krawczyk, Salvador García, et al. Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data[J]. Knowledge-Based Systems, 2016, 106: 251-263.（中科院一区）

[25]张忠良, 龚晟琛, 汪翼, 雒兴刚. 基于动态规划的联邦学习参与方选择优化方法[J]. 系统工程理论与实践, 2024, 44(12): 4064-4083.（管理科学部认定A类期刊）

[26]张忠良, 费秦君, 陈愉予, 雒兴刚. 基于BERT模型和动态集成选择的多分类文本情感识别研究[J]. 中国管理科学, 2024, 32(06): 140-150. （管理科学部认定A类期刊）

[27]雒兴刚, 刘伦, 张忠良, 张洪波. 考虑服务流程信息的服务配置优化方法[J]. 管理科学学报, 2023, 26(12): 1-18. （管理科学部认定A类期刊）

[28]雒兴刚, 张忠良, 阮渊鹏, 苑嘉航, 姬朋立. 基于管理视角的服务设计问题的研究综述与展望[J]. 系统工程理论与实践, 2021, 41(02): 400-410. （管理科学部认定A类期刊）

[29]张忠良, 陈愉予, 唐佳怡, 雒兴刚. 一种基于高斯过采样的集成学习算法[J]. 系统工程理论与实践, 2021, 41(02): 513-523. （管理科学部认定A类期刊）

[30]童珂凡（本科生）, 张忠良, 雒兴刚, 曾鸣, 汤建国. 基于动态分类器集成系统的卷烟感官质量预测方法[J]. 计算机应用与软件, 2020, 37(01): 66-70+81.（中文核心期刊）