Bing Zhu

According to our database, Bing Zhu authored at least 20 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

An Interpretable Anomaly Detection Model for Cloud POS Data.

Lin Gao, Yongzhen Fan, Duoxi Xiao, Yuanyuan Li, Bing Zhu, Zhengqian Sun

Proceedings of the 8th International Conference on Machine Learning and Soft Computing, 2024

2023

A bagging-based selective ensemble model for churn prediction on imbalanced data.

Bing Zhu, Cheng Qian, Seppe vanden Broucke, Jin Xiao, Yuanyuan Li

Expert Syst. Appl., October, 2023

Counterfactual learning in customer churn prediction under class imbalance.

Yuanyuan Li, Xue Song, Taicheng Wei, Bing Zhu

Proceedings of the 6th International Conference on Big Data Technologies, 2023

2022

A GAN-based hybrid sampling method for imbalanced customer classification.

Bing Zhu, Xin Pan, Seppe vanden Broucke, Jin Xiao

Inf. Sci., 2022

2021

An Aggregate Store Sales Forecasting Framework based on ConvLSTM.

Yanbing Liu, Keyi Lan, Feijie Huang, Xirong Cao, Boxing Feng, Bing Zhu

Proceedings of the ICCDA 2021: The 5th International Conference on Compute and Data Analysis, 2021

A LSTM-cBiGAN based hybrid sampling method for time series customer classification.

Xin Pan, Bing Zhu, Yanbing Liu, Feijie Huang

Proceedings of the DSIT 2021: 4th International Conference on Data Science and Information Technology, Shanghai, China, July 23, 2021

2020

An Experimental Investigation of Calibration Techniques for Imbalanced Data.

Lanlan Huang, Junkai Zhao, Bing Zhu, Hao Chen, Seppe K. L. M. vanden Broucke

IEEE Access, 2020

Customer classification based on spatial transition probability and Deep Forest.

Yanbing Liu, Xiang Shi, Feijie Huang, Senyou Yang, Qiqi Fan, Bing Zhu

Proceedings of the ACAI 2020: 3rd International Conference on Algorithms, 2020

2019

IRIC: An R library for binary imbalanced classification.

Bing Zhu, Zihan Gao, Junkai Zhao, Seppe K. L. M. vanden Broucke

SoftwareX, 2019

2018

Benchmarking sampling techniques for imbalance learning in churn prediction.

Bing Zhu, Bart Baesens, Aimée Backiel, Seppe K. L. M. vanden Broucke

J. Oper. Res. Soc., 2018

2017

A new transferred feature selection algorithm for customer identification.

Bing Zhu, Yongge Niu, Jin Xiao, Bart Baesens

Neural Comput. Appl., 2017

An empirical comparison of techniques for the class imbalance problem in churn prediction.

Bing Zhu, Bart Baesens, Seppe K. L. M. vanden Broucke

Inf. Sci., 2017

Improving Resampling-based Ensemble in Churn Prediction.

Bing Zhu, Seppe vanden Broucke, Bart Baesens, Sebastián Maldonado

Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, 2017

2016

Cluster ensemble framework based on the group method of data handling.

Ge-Er Teng, Changzheng He, Jin Xiao, Yue He, Bing Zhu, Xiaoyi Jiang

Appl. Soft Comput., 2016

2015

Customer choice prediction based on transfer learning.

Bing Zhu, Changzheng He, Xiaoyi Jiang

J. Oper. Res. Soc., 2015

A consistency-based validation for data clustering.

Bing Zhu, Changzheng He, Xiaoyi Jiang

Intell. Data Anal., 2015

Customers' Risk Type Prediction Based on Analog Complexing.

Changzheng He, Bing Zhu, Mingzhu Zhang, Yuanyuan Zhuang, Xiaoli He, Dongyue Du

Proceedings of the Third International Conference on Information Technology and Quantitative Management, 2015

2012

A GMDH-based fuzzy modeling approach for constructing TS model.

Bing Zhu, Changzheng He, Panos Liatsis, Xiaoyu Li

Fuzzy Sets Syst., 2012

A robust missing value imputation method for noisy data.

Bing Zhu, Changzheng He, Panos Liatsis

Appl. Intell., 2012

2011

Using Density based Score Fusion for Multimodal Identification Systems under the Missing Data Scenario.

Quang Duc Tran, Panos Liatsis, Bing Zhu, Changzheng He

Proceedings of the 2011 Developments in E-systems Engineering, 2011