Using Density based Score Fusion for Multimodal Identification Systems under the Missing Data Scenario. - Quang Duc Tran; Panos Liatsis; Bing Zhu 005; Changzheng He (DeSE, 2011)

A robust missing value imputation method for noisy data. - Bing Zhu 005; Changzheng He; Panos Liatsis (Appl. Intell., 2012)

A GMDH-based fuzzy modeling approach for constructing TS model. - Bing Zhu 005; Changzheng He; Panos Liatsis; Xiaoyu Li (Fuzzy Sets Syst., 2012)

Customers' Risk Type Prediction Based on Analog Complexing. - Changzheng He; Bing Zhu 005; Mingzhu Zhang; Yuanyuan Zhuang; Xiaoli He; Dongyue Du (ITQM, 2015)

A consistency-based validation for data clustering. - Bing Zhu 005; Changzheng He; Xiaoyi Jiang 001 (Intell. Data Anal., 2015)

Customer choice prediction based on transfer learning. - Bing Zhu 005; Changzheng He; Xiaoyi Jiang 001 (J. Oper. Res. Soc., 2015)

Cluster ensemble framework based on the group method of data handling. - Ge-Er Teng; Changzheng He; Jin Xiao; Yue He; Bing Zhu 005; Xiaoyi Jiang 001 (Appl. Soft Comput., 2016)

Improving Resampling-based Ensemble in Churn Prediction. - Bing Zhu 005; Seppe vanden Broucke; Bart Baesens; Sebastián Maldonado (LIDTA@PKDD/ECML, 2017)

An empirical comparison of techniques for the class imbalance problem in churn prediction. - Bing Zhu 005; Bart Baesens; Seppe K. L. M. vanden Broucke (Inf. Sci., 2017)

A new transferred feature selection algorithm for customer identification. - Bing Zhu 005; Yongge Niu; Jin Xiao; Bart Baesens (Neural Comput. Appl., 2017)

Benchmarking sampling techniques for imbalance learning in churn prediction. - Bing Zhu 005; Bart Baesens; Aimée Backiel; Seppe K. L. M. vanden Broucke (J. Oper. Res. Soc., 2018)

IRIC: An R library for binary imbalanced classification. - Bing Zhu 005; Zihan Gao; Junkai Zhao; Seppe K. L. M. vanden Broucke (SoftwareX, 2019)

Customer classification based on spatial transition probability and Deep Forest. - Yanbing Liu; Xiang Shi; Feijie Huang; Senyou Yang; Qiqi Fan; Bing Zhu 005 (ACAI, 2020)

An Experimental Investigation of Calibration Techniques for Imbalanced Data. - Lanlan Huang; Junkai Zhao; Bing Zhu 005; Hao Chen 088; Seppe K. L. M. vanden Broucke (IEEE Access, 2020)

A LSTM-cBiGAN based hybrid sampling method for time series customer classification. - Xin Pan; Bing Zhu 005; Yanbing Liu; Feijie Huang (DSIT, 2021)

An Aggregate Store Sales Forecasting Framework based on ConvLSTM. - Yanbing Liu; Keyi Lan; Feijie Huang; Xirong Cao; Boxing Feng; Bing Zhu 005 (ICCDA, 2021)

A GAN-based hybrid sampling method for imbalanced customer classification. - Bing Zhu 005; Xin Pan; Seppe vanden Broucke; Jin Xiao (Inf. Sci., 2022)

Counterfactual learning in customer churn prediction under class imbalance. - Yuanyuan Li; Xue Song; Taicheng Wei; Bing Zhu 005 (ICBDT, 2023)

A bagging-based selective ensemble model for churn prediction on imbalanced data. - Bing Zhu 005; Cheng Qian; Seppe vanden Broucke; Jin Xiao; Yuanyuan Li (Expert Syst. Appl., 2023)

Cluster-based Cross-Learning on Decomposed Components: A Novel Framework for SKU Sales Forecasting. - Duoxi Xiao; Jianjun Li; Sen Bai; Yongzhen Fan; Bing Zhu 005 (ICAAI, 2024)

A New Hybrid Churn Prediction Model Based on Logistic Regression and Extreme Gradient Boosting. - Yongzhen Fan; Guicai Xie; Bing Zhu 005 (ICISS, 2024)

An Interpretable Anomaly Detection Model for Cloud POS Data. - Lin Gao; Yongzhen Fan; Duoxi Xiao; Yuanyuan Li; Bing Zhu 005; Zhengqian Sun (ICMLSC, 2024)