Explainability in process outcome prediction: Guidelines to obtain interpretable and faithful models
Author
Abstract
Suggested Citation
DOI: 10.1016/j.ejor.2023.09.010
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Milind Dawande & Zhichao Feng & Ganesh Janakiraman, 2021. "On the Structure of Bottlenecks in Processes," Management Science, INFORMS, vol. 67(6), pages 3853-3870, June.
- Stephen C. Graves, 2021. "Reflections on the Evolution of Operations Management," Management Science, INFORMS, vol. 67(9), pages 5379-5388, September.
- Arno de Caigny & Kristof Coussement & Koen W. de Bock, 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," Post-Print hal-01741661, HAL.
- De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
- Koen W. de Bock & Arno de Caigny, 2021. "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print hal-03391564, HAL.
- Louis Geiler & Séverine Affeldt & Mohamed Nadif, 2022. "A survey on machine learning methods for churn prediction," Post-Print hal-03824873, HAL.
- Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
- De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
- Youngkeun Choi & Jae W. Choi, 2023. "Assessing the Predictive Performance of Machine Learning in Direct Marketing Response," International Journal of E-Business Research (IJEBR), IGI Global, vol. 19(1), pages 1-12, January.
- Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.
- Liu, Zhenkun & Zhang, Ying & Abedin, Mohammad Zoynul & Wang, Jianzhou & Yang, Hufang & Gao, Yuyang & Chen, Yinghao, 2024. "Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser prediction," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
- Chen, Yan & Zhang, Lei & Zhao, Yulu & Xu, Bing, 2022. "Implementation of penalized survival models in churn prediction of vehicle insurance," Journal of Business Research, Elsevier, vol. 153(C), pages 162-171.
- Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022.
"Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects,"
European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
- Elena Ivona Dumitrescu & Sullivan Hué & Christophe Hurlin & Sessi Tokpavi, 2022. "Machine Learning for Credit Scoring: Improving Logistic Regression with Non Linear Decision Tree Effects," Post-Print hal-03331114, HAL.
- Narendra Singh & Pushpa Singh & Mukul Gupta, 2020. "An inclusive survey on machine learning for CRM: a paradigm shift," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 447-457, December.
- Liu, Zhenkun & Jiang, Ping & De Bock, Koen W. & Wang, Jianzhou & Zhang, Lifang & Niu, Xinsong, 2024. "Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
- Ni, Ji & Chen, Bowei & Allinson, Nigel M. & Ye, Xujiong, 2020. "A hybrid model for predicting human physical activity status from lifelogging data," European Journal of Operational Research, Elsevier, vol. 281(3), pages 532-542.
- Lewlisa Saha & Hrudaya Kumar Tripathy & Tarek Gaber & Hatem El-Gohary & El-Sayed M. El-kenawy, 2023. "Deep Churn Prediction Method for Telecommunication Industry," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
- Wen Zhang & Andrew Dunkley & Urvi Kanabar & David Elliott & Henry P. Wynn, 2022. "A decision support system for liability in civil litigation: a case study from an insurance company," Annals of Operations Research, Springer, vol. 315(2), pages 695-706, August.
- Andreea Dumitrache & Monica Mihaela Maer Matei, 2019. "Churn Analysis in a Romanian Telecommunications Company," Postmodern Openings, Editura Lumen, Department of Economics, vol. 10(4), pages 44-53, December.
- Tianyuan Zhang & Sérgio Moro & Ricardo F. Ramos, 2022. "A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation," Future Internet, MDPI, vol. 14(3), pages 1-19, March.
- Ebru Pekel Ozmen & Tuncay Ozcan, 2022. "A novel deep learning model based on convolutional neural networks for employee churn prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 539-550, April.
- Borchert, Philipp & Coussement, Kristof & De Caigny, Arno & De Weerdt, Jochen, 2023. "Extending business failure prediction models with textual website content using deep learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 348-357.
- Kraus, Mathias & Tschernutter, Daniel & Weinzierl, Sven & Zschech, Patrick, 2024. "Interpretable generalized additive neural networks," European Journal of Operational Research, Elsevier, vol. 317(2), pages 303-316.
More about this item
Keywords
Data science; Explainable artificial intelligence; Process outcome prediction; Interpretability; Faithfulness;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:317:y:2024:i:2:p:317-329. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.