{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T05:05:58Z","timestamp":1764997558792,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":18,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities","award":["SXYPY202337"],"award-info":[{"award-number":["SXYPY202337"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,9,22]]},"DOI":"10.1145\/3627377.3627392","type":"proceedings-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T12:08:25Z","timestamp":1701691705000},"page":"96-102","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Counterfactual learning in customer churn prediction under class imbalance"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5736-6392","authenticated-orcid":false,"given":"Yuanyuan","family":"Li","sequence":"first","affiliation":[{"name":"Sichuan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0716-1645","authenticated-orcid":false,"given":"Xue","family":"Song","sequence":"additional","affiliation":[{"name":"Sichuan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8161-0227","authenticated-orcid":false,"given":"Taicheng","family":"Wei","sequence":"additional","affiliation":[{"name":"Guangxi Tobacco Industrial Co.,Ltd, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0610-9317","authenticated-orcid":false,"given":"Bing","family":"Zhu","sequence":"additional","affiliation":[{"name":"Sichuan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Nice: an algorithm for nearest instance counterfactual explanations. Data Mining and Knowledge Discovery","author":"Brughmans Dieter","year":"2023","unstructured":"Dieter Brughmans, Pieter Leyman, and David Martens. 2023. Nice: an algorithm for nearest instance counterfactual explanations. Data Mining and Knowledge Discovery (2023), 1\u201339."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2018.07.005"},{"key":"e_1_3_2_1_3_1","volume-title":"Interpretable ML for Imbalanced Data. arXiv preprint arXiv:2212.07743","author":"Dablain A","year":"2022","unstructured":"Damien\u00a0A Dablain, Colin Bellinger, Bartosz Krawczyk, David\u00a0W Aha, and Nitesh\u00a0V Chawla. 2022. Interpretable ML for Imbalanced Data. arXiv preprint arXiv:2212.07743 (2022)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58112-1_31"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3177783"},{"key":"e_1_3_2_1_6_1","volume-title":"Explaining data-driven decisions made by AI systems: the counterfactual approach. arXiv preprint arXiv:2001.07417","author":"Fern\u00e1ndez-Lor\u00eda Carlos","year":"2020","unstructured":"Carlos Fern\u00e1ndez-Lor\u00eda, Foster Provost, and Xintian Han. 2020. Explaining data-driven decisions made by AI systems: the counterfactual approach. arXiv preprint arXiv:2001.07417 (2020)."},{"key":"e_1_3_2_1_7_1","volume-title":"Counterfactual explanations and how to find them: literature review and benchmarking. Data Mining and Knowledge Discovery","author":"Guidotti Riccardo","year":"2022","unstructured":"Riccardo Guidotti. 2022. Counterfactual explanations and how to find them: literature review and benchmarking. Data Mining and Knowledge Discovery (2022), 1\u201355."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07418-8"},{"key":"e_1_3_2_1_9_1","volume-title":"If only we had better counterfactual explanations: Five key deficits to rectify in the evaluation of counterfactual xai techniques. arXiv preprint arXiv:2103.01035","author":"Keane T","year":"2021","unstructured":"Mark\u00a0T Keane, Eoin\u00a0M Kenny, Eoin Delaney, and Barry Smyth. 2021. If only we had better counterfactual explanations: Five key deficits to rectify in the evaluation of counterfactual xai techniques. arXiv preprint arXiv:2103.01035 (2021)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462597"},{"key":"e_1_3_2_1_11_1","volume-title":"Inverse classification for comparison-based interpretability in machine learning. arXiv preprint arXiv:1712.08443","author":"Laugel Thibault","year":"2017","unstructured":"Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. 2017. Inverse classification for comparison-based interpretability in machine learning. arXiv preprint arXiv:1712.08443 (2017)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-91473-2_9"},{"key":"e_1_3_2_1_13_1","volume-title":"Explaining data-driven document classifications. MIS quarterly 38, 1","author":"Martens David","year":"2014","unstructured":"David Martens and Foster Provost. 2014. Explaining data-driven document classifications. MIS quarterly 38, 1 (2014), 73\u2013100."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375850"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.917504"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287566"},{"key":"e_1_3_2_1_17_1","first-page":"841","article-title":"Counterfactual explanations without opening the black box: Automated decisions and the GDPR","volume":"31","author":"Wachter Sandra","year":"2017","unstructured":"Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2017. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harv. JL & Tech. 31 (2017), 841.","journal-title":"Harv. JL & Tech."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2019.01.032"}],"event":{"name":"ICBDT 2023: 2023 6th International Conference on Big Data Technologies","acronym":"ICBDT 2023","location":"Qingdao China"},"container-title":["Proceedings of the 2023 6th International Conference on Big Data Technologies"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627377.3627392","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627377.3627392","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T16:20:09Z","timestamp":1755879609000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627377.3627392"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,22]]},"references-count":18,"alternative-id":["10.1145\/3627377.3627392","10.1145\/3627377"],"URL":"https:\/\/doi.org\/10.1145\/3627377.3627392","relation":{},"subject":[],"published":{"date-parts":[[2023,9,22]]},"assertion":[{"value":"2023-12-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}