Editorial Team

Dr. Kaixiang Yang works as the Associate Professor at the School of Computer Science and Engineering, South China University of Technology, China. He engages in machine learning and data mining research, focusing on the analysis and application of complex multi-source heterogeneous data.

He has been invited as a guest editorial board member of Computer Communications, Mathematics, and other journals. His research results have been published in IEEE Trans. on Neural Networks and Learning, IEEE Trans. on Systems, and IEEE Trans. on Data Mining. Systems, IEEE Trans. on Knowledge and Data Engineering, IEEE Trans. on Systems, Man, and IEEE Trans. on Knowledge and Data Engineering, IEEE Trans. on Systems, Man, and Cybernetics: System, ICASSP, and so on.

Publications:

[1] Yang K, Liu Y, Yu Z, et al. et al., Extracting and Composing Robust Features with Broad LearningSystem. IEEE Transactions on Knowledge and Data Engineering, 2022. Early Access.[2] Yang K, Yu Z, Chen C L P, et al. Progressive hybrid classifier ensemble for imbalanced data. IEEETransactions on Systems, Man, and Cybernetics: System, 2022, 52(4): 2464-2478.[3] Yang K, Shi Y, Yu Z, et al. Stacked One-Class Broad Learning System for Intrusion Detection inIndustry 4.0. IEEE Transactions on Industrial Informatics, 2022. Early Access.[4] Yang K, Yu Z, Wen X, et al. Hybrid Classifier Ensemble for Imbalanced Data. IEEE Transactions onNeural Networks and Learning Systems, 2019, 31(4): 1387-1400.[5] Yang K, Yu Z, Chen C L P, et al. Incremental Weighted Ensemble Broad Learning System forImbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 2021. Early Access.[6] Yu Z, Zhong Z, Yang K*, Cao W and Chen C L P, "Broad Learning Autoencoder with GraphStructure for Data Clustering," in IEEE Transactions on Knowledge and Data Engineering, doi:10.1109/TKDE.2023.3283425.[7] Zhu C, Pu Y, Yang K*, Yang Q and Chen C L P, "Distributed Optical Fiber Intrusion Detection byImage Encoding and SwinT in Multi-Interference Environment of Long-Distance Pipeline," inIEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-12, 2023, Art no. 2515012,doi: 10.1109/TIM.2023.3277937.[8] Shi Y., Yang K*, Yu Z, Chen C L P and Zeng H, "Adaptive Ensemble Clustering With BoostingBLS-Based Autoencoder," in IEEE Transactions on Knowledge and Data Engineering, doi:10.1109/TKDE.2023.3271120.[9] Yu Z, Ye F, Yang K*, et al. Semi-supervised Classification with Novel Graph Construction forHigh-Dimensional Data. IEEE Transactions on Neural Networks and Learning Systems, 2022,33(1): 75-88.[10] Liang Z, Wang H, Yang K*, et al. Adaptive Fusion Based Method for Imbalanced DataClassification. Frontiers in Neurorobotics, 2022, 16: 827913.[11] Chen W, Yang K*, Yu Z, et al. Double-kernel based class-specific broad learning system formulticlass imbalance learning[J]. Knowledge-Based Systems, 2022, 253: 109535.[12] Zhu C, Yang K*, Yang Q, et al. A comprehensive bibliometric analysis of signal processing andpattern recognition based on distributed optical fiber[J]. Measurement, 2022: 112340.[13] Chen W, Yang K*, Zhang W, et al. Double-kernelized weighted broad learning system forimbalanced data[J]. Neural Computing and Applications, 2022, 34(22): 19923-19936.[14] Y. Shi, K. Yang*, Z. Yu, et al. Boosted Unsupervised Feature Selection for Tumor Gene ExpressionProfiles [J]. CAAI Transactions on Intelligence Technology, 2023. (Accepted)[15] Yang K, Shi Y, Yu Z, et al. Hybrid Clustering Solutions Fusion based on Gated Three-wayDecision[C]//2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023: 1-10.[16] Zhu C, Yang K*, Yang Q, et al. Visibility and meteorological parameter model based on rashomonregression analysis[C]//2022 12th International Conference on Information Science and Technology(ICIST). IEEE, 2022: 367-373.[17] Liu Y, Yang K*, Yu Z, et al. Pruning Broad Learning System based on Adaptive Feature Evolution.2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021.[18] Li Z, Yu Z, Yang K*, et al. Local Tangent Generative Adversarial Network for Imbalanced DataClassification. 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021.[19] Chen W, Yang K*, Shi Y, et al. Kernel-based Class-specific Broad Learning System for softwaredefect prediction. 2021 8th International Conference on Information, Cybernetics, andComputational Social Systems (ICCSS). IEEE, 2021.[20] Shi Y, Chen D, Chen L, Yang K, et al. Ensemble Clustering Based on Manifold Broad LearningSystem[C]//2022 China Automation Congress (CAC). IEEE, 2022: 2227-2232.[21] Q. Deng, J. Shen, H. Jiang, K. Yang, X. Wang and Q. Yang, "Third-party construction intrusiondetection of natural gas pipelines based on improved YOLOv5," 2022 China Automation Congress(CAC), Xiamen, China, 2022, pp. 1844-1849, doi: 10.1109/CAC57257.2022.10054804.[22] Wang M, Liu B, Bi J, Yang K, et al. Disturbance Observer-based Model Predictive Control forDiscrete-time Linear Systems with Bounded Disturbances[C]//2022 41st Chinese ControlConference (CCC). IEEE, 2022: 2700-2704.[23] Xiang X, Shen J, Yang K, et al. Daily natural gas load forecasting based on sequenceautocorrelation[C]//2022 37th Youth Academic Annual Conference of Chinese Association ofAutomation (YAC). IEEE, 2022: 1452-1459.[24] Wang M, Zhao C, Yang K, et al. DOB-based Event-triggered Receding Horizon Control forDiscrete-time Linear Systems with Bounded Disturbances[C]//2022 37th Youth Academic AnnualConference of Chinese Association of Automation (YAC). IEEE, 2022: 812-817.[25] Li L, Bi J, Yang K, et al. MGC-GAN: Multi-Graph Convolutional Generative AdversarialNetworks for Accurate Citywide Traffic Flow Prediction[C]//2022 IEEE International Conferenceon Systems, Man, and Cybernetics (SMC). IEEE, 2022: 2557-2562.[26] Li L, Bi J, Yang K, et al. Spatial-Temporal Semantic Generative Adversarial Networks for FlexibleMulti-step Urban Flow Prediction[C]//International Conference on Artificial Neural Networks.Cham: Springer Nature Switzerland, 2022: 763-775.[27] Chen W, Zhang W, Yang K, et al. Exploring industrial evolution with correlation analysis andsmoothing forecasting: a case of marine industry in Guangdong Province of South China. 202213th International Conference on E-Education, E-Business, E-Management, and E-Learning (IC4E),2022.[28] Dong C, Ye Q, Meng W, Yang K, Few-shot learning with improved local representations via biasrectify module. 2022 IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP). IEEE, 2022.[29] Lan K, Yang K*, Yu Z, Han G, et al. Adaptive Weighted Broad Learning System for softwaredefect prediction. 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020.[30] Ye S, Yu Z, Lin Jia, Yang K，et al. Two-Dimensional-Reduction Random Forest. 2018 EighthInternational Conference on Information Science and Technology (ICIST). IEEE, 2018.