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Bing Zhu 0005
Person information
- affiliation (PhD 2011): Sichuan University, School of Business, Chengdu, China
- not to be confused with: Bing Hu 0003
Other persons with the same name
- Bing Zhu — disambiguation page
- Bing Zhu 0001 — University of California San Diego, Institute for Neural Computation, La Jolla, CA, USA
- Bing Zhu 0002 — Jinggangshan University, Computer Science Department, Ji'an, China
- Bing Zhu 0003
— Peking University, School of Electronic and Computer Engineering, Shenzhen, China
- Bing Zhu 0004
- Bing Zhu 0006
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2020 – today
- 2024
- [c8]Lin Gao
An Interpretable Anomaly Detection Model for Cloud POS Data. ICMLSC 2024: 83-90 - 2023
- [j12]Bing Zhu
A bagging-based selective ensemble model for churn prediction on imbalanced data. Expert Syst. Appl. 227: 120223 (2023) - [c7]Yuanyuan Li
Counterfactual learning in customer churn prediction under class imbalance. ICBDT 2023: 96-102 - 2022
- [j11]Bing Zhu, Xin Pan, Seppe vanden Broucke, Jin Xiao:
A GAN-based hybrid sampling method for imbalanced customer classification. Inf. Sci. 609: 1397-1411 (2022) - 2021
- [c6]Xin Pan, Bing Zhu, Yanbing Liu, Feijie Huang:
A LSTM-cBiGAN based hybrid sampling method for time series customer classification. DSIT 2021: 74-77 - [c5]Yanbing Liu, Keyi Lan, Feijie Huang, Xirong Cao, Boxing Feng, Bing Zhu:
An Aggregate Store Sales Forecasting Framework based on ConvLSTM. ICCDA 2021: 67-72 - 2020
- [j10]Lanlan Huang
An Experimental Investigation of Calibration Techniques for Imbalanced Data. IEEE Access 8: 127343-127352 (2020) - [c4]Yanbing Liu, Xiang Shi, Feijie Huang, Senyou Yang, Qiqi Fan, Bing Zhu:
Customer classification based on spatial transition probability and Deep Forest. ACAI 2020: 39:1-39:5
2010 – 2019
- 2019
- [j9]Bing Zhu, Zihan Gao, Junkai Zhao, Seppe K. L. M. vanden Broucke:
IRIC: An R library for binary imbalanced classification. SoftwareX 10: 100341 (2019) - 2018
- [j8]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): 49-65 (2018) - 2017
- [j7]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: 84-99 (2017) - [j6]Bing Zhu
A new transferred feature selection algorithm for customer identification. Neural Comput. Appl. 28(9): 2593-2603 (2017) - [c3]Bing Zhu, Seppe vanden Broucke, Bart Baesens, Sebastián Maldonado:
Improving Resampling-based Ensemble in Churn Prediction. LIDTA@PKDD/ECML 2017: 79-91 - 2016
- [j5]Ge-Er Teng, Changzheng He, Jin Xiao, Yue He, Bing Zhu
Cluster ensemble framework based on the group method of data handling. Appl. Soft Comput. 43: 35-46 (2016) - 2015
- [j4]Bing Zhu
A consistency-based validation for data clustering. Intell. Data Anal. 19(3): 471-484 (2015) - [j3]Bing Zhu
Customer choice prediction based on transfer learning. J. Oper. Res. Soc. 66(6): 1044-1051 (2015) - [c2]Changzheng He, Bing Zhu
Customers' Risk Type Prediction Based on Analog Complexing. ITQM 2015: 939-943 - 2012
- [j2]Bing Zhu
A robust missing value imputation method for noisy data. Appl. Intell. 36(1): 61-74 (2012) - [j1]Bing Zhu
A GMDH-based fuzzy modeling approach for constructing TS model. Fuzzy Sets Syst. 189(1): 19-29 (2012) - 2011
- [c1]Quang Duc Tran, Panos Liatsis, Bing Zhu
Using Density based Score Fusion for Multimodal Identification Systems under the Missing Data Scenario. DeSE 2011: 238-242
Coauthor Index
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