Jianjun Zhang

Orcid: 0000-0001-9133-4994

Affiliations:
  • South China University of Technology, Guangzhou, China


According to our database1, Jianjun Zhang authored at least 25 papers between 2016 and 2024.

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Bibliography

2024
BASS: Broad Network Based on Localized Stochastic Sensitivity.
IEEE Trans. Neural Networks Learn. Syst., February, 2024

2023
KNNENS: A k-Nearest Neighbor Ensemble-Based Method for Incremental Learning Under Data Stream With Emerging New Classes.
IEEE Trans. Neural Networks Learn. Syst., November, 2023

Perturbation-based oversampling technique for imbalanced classification problems.
Int. J. Mach. Learn. Cybern., March, 2023

Robust recurrent neural networks for time series forecasting.
Neurocomputing, March, 2023

Evaluation of radial basis function neural network minimizing L-GEM for sensor-based activity recognition.
J. Ambient Intell. Humaniz. Comput., 2023

2022
Hashing-Based Undersampling Ensemble for Imbalanced Pattern Classification Problems.
IEEE Trans. Cybern., 2022

Ensembling perturbation-based oversamplers for imbalanced datasets.
Neurocomputing, 2022

Stochastic Sensitivity Regularized Autoencoder for Robust Feature Learning.
Proceedings of the 21st 2022 International Conference on Cognitive Informatics & Cognitive Computing, 2022

Hashing-based Undersampling for Large Scale Histopathology Image Classification.
Proceedings of the 21st 2022 International Conference on Cognitive Informatics & Cognitive Computing, 2022

2021
Stochastic Sensitivity Tree Boosting for Imbalanced Prediction Problems of Protein-Ligand Interaction Sites.
IEEE Trans. Emerg. Top. Comput. Intell., 2021

Maximizing minority accuracy for imbalanced pattern classification problems using cost-sensitive Localized Generalization Error Model.
Appl. Soft Comput., 2021

2020
Training error and sensitivity-based ensemble feature selection.
Int. J. Mach. Learn. Cybern., 2020

Minority Oversampling Using Sensitivity.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

2019
New Appliance Detection for Nonintrusive Load Monitoring.
IEEE Trans. Ind. Informatics, 2019

Cost-Sensitive Weighting and Imbalance-Reversed Bagging for Streaming Imbalanced and Concept Drifting in Electricity Pricing Classification.
IEEE Trans. Ind. Informatics, 2019

Undersampling Near Decision Boundary for Imbalance Problems.
Proceedings of the 2019 International Conference on Machine Learning and Cybernetics, 2019

2018
Stochastic Sensitivity Measure-Based Noise Filtering and Oversampling Method for Imbalanced Classification Problems.
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2018

Imbalanced High-Frequency Number Classification Based on DSUS.
Proceedings of the 2018 International Conference on Machine Learning and Cybernetics, 2018

Loan Default Prediction Using Diversified Sensitivity Undersampling.
Proceedings of the 2018 International Conference on Machine Learning and Cybernetics, 2018

2017
Human Activity Recognition Using Radial Basis Function Neural Network Trained via a Minimization of Localized Generalization Error.
Proceedings of the Ubiquitous Computing and Ambient Intelligence, 2017

Bsmboost for imbalanced pattern classification problems.
Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics, 2017

Feature Weighting for RBFNN Based on Genetic Algorithm and Localized Generalization Error Model.
Proceedings of the 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, 2017

Weighted ensemble of Diversified Sensitivity-based undersampling for imbalanced pattern classification problems.
Proceedings of the 2017 International Conference on Machine Learning and Cybernetics, 2017

2016
Dual autoencoders features for imbalance classification problem.
Pattern Recognit., 2016

Effects of different base classifiers to Learn++ family algorithms for concept drifting and imbalanced pattern classification problems.
Proceedings of the International Conference on Machine Learning and Cybernetics, 2016


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