{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T09:32:56Z","timestamp":1743154376502,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031424298"},{"type":"electronic","value":"9783031424304"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-42430-4_28","type":"book-chapter","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T15:03:54Z","timestamp":1695913434000},"page":"339-351","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparative Study on\u00a0Customer Churn Prediction by\u00a0Using Machine Learning Techniques"],"prefix":"10.1007","author":[{"given":"Shashikant","family":"Kumar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1678-3527","authenticated-orcid":false,"given":"Doina","family":"Logofatu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,29]]},"reference":[{"issue":"1","key":"28_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0191-6","volume":"6","author":"AK Ahmad","year":"2019","unstructured":"Ahmad, A.K., Jafar, A., Aljoumaa, K.: Customer churn prediction in telecom using machine learning in big data platform. J. Big Data 6(1), 1\u201324 (2019). https:\/\/doi.org\/10.1186\/s40537-019-0191-6","journal-title":"J. Big Data"},{"key":"28_CR2","doi-asserted-by":"publisher","unstructured":"Ahmad, A.K., Jafar, A., Aljoumaa, K.: Customer churn prediction in telecom using machine learning in big data platform. J. Big Data 6(1), 28 (2019). https:\/\/doi.org\/10.1186\/s40537-019-0191-6. https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-019-0191-6","DOI":"10.1186\/s40537-019-0191-6"},{"key":"28_CR3","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/1007730.1007735","volume":"6","author":"G Batista","year":"2004","unstructured":"Batista, G., Prati, R., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. 6, 20\u201329 (2004). https:\/\/doi.org\/10.1145\/1007730.1007735","journal-title":"SIGKDD Explor."},{"key":"28_CR4","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.ejor.2003.12.010","volume":"164","author":"W Buckinx","year":"2005","unstructured":"Buckinx, W., Van den Poel, D.: Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. Eur. J. Oper. Res. 164, 252\u2013268 (2005). https:\/\/doi.org\/10.1016\/j.ejor.2003.12.010","journal-title":"Eur. J. Oper. Res."},{"issue":"3","key":"28_CR5","doi-asserted-by":"publisher","first-page":"4626","DOI":"10.1016\/j.eswa.2008.05.027","volume":"36","author":"J Burez","year":"2009","unstructured":"Burez, J., Van den Poel, D.: Handling class imbalance in customer churn prediction. Expert Syst. Appl. 36(3), 4626\u20134636 (2009)","journal-title":"Expert Syst. Appl."},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Chawla, N., Bowyer, K., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. ArXiv abs\/1106.1813 (2002)","DOI":"10.1613\/jair.953"},{"key":"28_CR7","series-title":"Algorithms for Intelligent Systems","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-981-15-5243-4_12","volume-title":"Advances in Machine Learning and Computational Intelligence","author":"H Jain","year":"2021","unstructured":"Jain, H., Yadav, G., Manoov, R.: Churn prediction and retention in banking, telecom and IT sectors using machine learning techniques. In: Patnaik, S., Yang, X.-S., Sethi, I.K. (eds.) Advances in Machine Learning and Computational Intelligence. AIS, pp. 137\u2013156. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-15-5243-4_12"},{"key":"28_CR8","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/978-3-030-20257-6_25","volume-title":"Engineering Applications of Neural Networks","author":"S Kumar","year":"2019","unstructured":"Kumar, S., Kumar, M.: Predicting customer churn using artificial neural network. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds.) EANN 2019. CCIS, vol. 1000, pp. 299\u2013306. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20257-6_25"},{"key":"28_CR9","doi-asserted-by":"publisher","unstructured":"Andrews, R.: Churn prediction in telecom sector using machine learning. Int. J. Inf. Syst. Comput. Sci. 8(2), 132\u2013134 (2019). https:\/\/doi.org\/10.30534\/ijiscs\/2019\/31822019. http:\/\/www.warse.org\/IJISCS\/static\/pdf\/file\/ijiscs31822019.pdf","DOI":"10.30534\/ijiscs\/2019\/31822019"},{"key":"28_CR10","doi-asserted-by":"publisher","unstructured":"Pebrianti, D., Istinabiyah, D.D., Bayuaji, L., Rusdah: Hybrid method for churn prediction model in the case of telecommunication companies. In: 2022 9th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), pp. 161\u2013166 (2022). https:\/\/doi.org\/10.23919\/EECSI56542.2022.9946535","DOI":"10.23919\/EECSI56542.2022.9946535"},{"key":"28_CR11","doi-asserted-by":"publisher","unstructured":"Qureshi, S.A., Rehman, A.S., Qamar, A.M., Kamal, A., Rehman, A.: Telecommunication subscribers\u2019 churn prediction model using machine learning. In: Eighth International Conference on Digital Information Management (ICDIM 2013), pp. 131\u2013136 (2013). https:\/\/doi.org\/10.1109\/ICDIM.2013.6693977","DOI":"10.1109\/ICDIM.2013.6693977"},{"key":"28_CR12","first-page":"71","volume":"10","author":"UR Salunkhe","year":"2018","unstructured":"Salunkhe, U.R., Mali, S.N.: A hybrid approach for class imbalance problem in customer churn prediction: a novel extension to under-sampling. Int. J. Intell. Syst. Appl. 10, 71\u201381 (2018)","journal-title":"Int. J. Intell. Syst. Appl."},{"key":"28_CR13","first-page":"693","volume":"2","author":"E Shaaban","year":"2012","unstructured":"Shaaban, E., Helmy, Y., Khedr, A., Nasr, M.: A proposed churn prediction model. Int. J. Eng. Res. Appl. (IJERA) 2, 693\u2013697 (2012)","journal-title":"Int. J. Eng. Res. Appl. (IJERA)"},{"key":"28_CR14","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/978-981-15-0324-5_20","volume-title":"International Conference on Innovative Computing and Communications","author":"T Sharma","year":"2020","unstructured":"Sharma, T., Gupta, P., Nigam, V., Goel, M.: Customer churn prediction in telecommunications using gradient boosted trees. In: Khanna, A., Gupta, D., Bhattacharyya, S., Snasel, V., Platos, J., Hassanien, A.E. (eds.) International Conference on Innovative Computing and Communications. AISC, vol. 1059, pp. 235\u2013246. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-0324-5_20"},{"key":"28_CR15","doi-asserted-by":"publisher","unstructured":"Shitole, A., Priyadarshini, I.: Survey of machine learning algorithms & its applications (2021). https:\/\/doi.org\/10.5281\/zenodo.5090570","DOI":"10.5281\/zenodo.5090570"},{"key":"28_CR16","doi-asserted-by":"publisher","unstructured":"Shumaly, S., Neysaryan, P., Guo, Y.: Handling class imbalance in customer churn prediction in telecom sector using sampling techniques, bagging and boosting trees. In: 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 082\u2013087 (2020). https:\/\/doi.org\/10.1109\/ICCKE50421.2020.9303698","DOI":"10.1109\/ICCKE50421.2020.9303698"},{"key":"28_CR17","doi-asserted-by":"publisher","unstructured":"Umayaparvathi, V., Iyakutti, K.: Applications of data mining techniques in telecom churn prediction. Int. J. Comput. Appl. 42(20), 5\u20139 (2012). https:\/\/doi.org\/10.5120\/5814-8122. http:\/\/research.ijcaonline.org\/volume42\/number20\/pxc3878122.pdf","DOI":"10.5120\/5814-8122"},{"key":"28_CR18","doi-asserted-by":"publisher","unstructured":"Verbeke, W., Dejaeger, K., Martens, D., Hur, J., Baesens, B.: New insights into churn prediction in the telecommunication sector: a profit driven data mining approach. Eur. J. Oper. Res. 218(1), 211\u2013229 (2012). https:\/\/doi.org\/10.1016\/j.ejor.2011.09.031. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0377221711008599","DOI":"10.1016\/j.ejor.2011.09.031"},{"key":"28_CR19","doi-asserted-by":"publisher","unstructured":"Xia, G.E., Wang, H., Jiang, Y.: Application of customer churn prediction based on weighted selective ensembles. In: 2016 3rd International Conference on Systems and Informatics (ICSAI), pp. 513\u2013519 (2016). https:\/\/doi.org\/10.1109\/ICSAI.2016.7811009","DOI":"10.1109\/ICSAI.2016.7811009"}],"container-title":["Communications in Computer and Information Science","Recent Challenges in Intelligent Information and Database Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-42430-4_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T15:10:51Z","timestamp":1695913851000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-42430-4_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031424298","9783031424304"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-42430-4_28","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"29 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Intelligent Information and Database Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Phuket","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aciids2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aciids.pwr.edu.pl\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"224","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,87","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,82","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}