Bing Zhu

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 11

Top Topics

Selection Algorithm

Class Imbalance

Cluster Ensemble

Imbalanced Datasets

Top Venues

J. Oper. Res. Soc.

Publications

Lin Gao, Yongzhen Fan, Duoxi Xiao, Yuanyuan Li, Bing Zhu, Zhengqian Sun
An Interpretable Anomaly Detection Model for Cloud POS Data. ICMLSC (2024)
Yuanyuan Li, Xue Song, Taicheng Wei, Bing Zhu
Counterfactual learning in customer churn prediction under class imbalance. ICBDT (2023)
Bing Zhu, Cheng Qian, Seppe vanden Broucke, Jin Xiao, Yuanyuan Li
A bagging-based selective ensemble model for churn prediction on imbalanced data. Expert Syst. Appl. 227 (2023)
Bing Zhu, Xin Pan, Seppe vanden Broucke, Jin Xiao
A GAN-based hybrid sampling method for imbalanced customer classification. Inf. Sci. 609 (2022)
Lanlan Huang, Junkai Zhao, Bing Zhu, Hao Chen, Seppe K. L. M. vanden Broucke
An Experimental Investigation of Calibration Techniques for Imbalanced Data. IEEE Access 8 (2020)
Bing Zhu, Zihan Gao, Junkai Zhao, Seppe K. L. M. vanden Broucke
IRIC: An R library for binary imbalanced classification. SoftwareX 10 (2019)
Bing Zhu, Bart Baesens, Aimée Backiel, Seppe K. L. M. vanden Broucke
Benchmarking sampling techniques for imbalance learning in churn prediction. J. Oper. Res. Soc. 69 (1) (2018)
Bing Zhu, Bart Baesens, Seppe K. L. M. vanden Broucke
An empirical comparison of techniques for the class imbalance problem in churn prediction. Inf. Sci. 408 (2017)
Bing Zhu, Yongge Niu, Jin Xiao, Bart Baesens
A new transferred feature selection algorithm for customer identification. Neural Comput. Appl. 28 (9) (2017)
Bing Zhu, Seppe vanden Broucke, Bart Baesens, Sebastián Maldonado
Improving Resampling-based Ensemble in Churn Prediction. LIDTA@PKDD/ECML (2017)
Ge-Er Teng, Changzheng He, Jin Xiao, Yue He, Bing Zhu, Xiaoyi Jiang
Cluster ensemble framework based on the group method of data handling. Appl. Soft Comput. 43 (2016)
Bing Zhu, Changzheng He, Xiaoyi Jiang
A consistency-based validation for data clustering. Intell. Data Anal. 19 (3) (2015)
Changzheng He, Bing Zhu, Mingzhu Zhang, Yuanyuan Zhuang, Xiaoli He, Dongyue Du
Customers' Risk Type Prediction Based on Analog Complexing. ITQM (2015)
Bing Zhu, Changzheng He, Xiaoyi Jiang
Customer choice prediction based on transfer learning. J. Oper. Res. Soc. 66 (6) (2015)
Bing Zhu, Changzheng He, Panos Liatsis
A robust missing value imputation method for noisy data. Appl. Intell. 36 (1) (2012)
Bing Zhu, Changzheng He, Panos Liatsis, Xiaoyu Li
A GMDH-based fuzzy modeling approach for constructing TS model. Fuzzy Sets Syst. 189 (1) (2012)
Quang Duc Tran, Panos Liatsis, Bing Zhu, Changzheng He
Using Density based Score Fusion for Multimodal Identification Systems under the Missing Data Scenario. DeSE (2011)