{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:40:44Z","timestamp":1761129644316},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030302405"},{"type":"electronic","value":"9783030302412"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30241-2_55","type":"book-chapter","created":{"date-parts":[[2019,8,31]],"date-time":"2019-08-31T09:30:41Z","timestamp":1567243841000},"page":"663-674","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Warranty Claim Rate Prediction Using Logged Vehicle Data"],"prefix":"10.1007","author":[{"given":"Reza","family":"Khoshkangini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sepideh","family":"Pashami","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Slawomir","family":"Nowaczyk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,8,30]]},"reference":[{"issue":"10","key":"55_CR1","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1093\/bioinformatics\/btq134","volume":"26","author":"A Altmann","year":"2010","unstructured":"Altmann, A., Tolo\u015fi, L., Sander, O., Lengauer, T.: Permutation importance: a corrected feature importance measure. Bioinformatics 26(10), 1340\u20131347 (2010)","journal-title":"Bioinformatics"},{"key":"55_CR2","doi-asserted-by":"crossref","unstructured":"Behrens, T., Zhu, A.X., Schmidt, K., Scholten, T.: Multi-scale digital terrain analysis and feature selection for digital soil mapping. Geoderma (2010)","DOI":"10.1016\/j.geoderma.2009.07.010"},{"issue":"7","key":"55_CR3","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","volume":"30","author":"AP Bradley","year":"1997","unstructured":"Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145\u20131159 (1997)","journal-title":"Pattern Recogn."},{"key":"55_CR4","doi-asserted-by":"publisher","DOI":"10.1201\/9781315139470","volume-title":"Classification and Regression Trees","author":"L Breiman","year":"2017","unstructured":"Breiman, L.: Classification and Regression Trees. Routledge, New York (2017)"},{"key":"55_CR5","unstructured":"Buitinck, L., Louppe, G.: API design for machine learning software: Experiences from the scikit-learn project (2013)"},{"key":"55_CR6","unstructured":"Chen, J., Lynn, N., Singpurwalla, N.: Forecasting warranty claims (1996)"},{"key":"55_CR7","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1142\/9789812703378_0009","volume":"10","author":"S Chukova","year":"2005","unstructured":"Chukova, S., Robinson, J.: Estimating mean cumulative functions from truncated automotive warranty data. Modern Stat. Math. Methods Reliab. 10, 121 (2005)","journal-title":"Modern Stat. Math. Methods Reliab."},{"key":"55_CR8","doi-asserted-by":"crossref","unstructured":"Corbu, D., Chukova, S., O\u2019Sullivan, J.: Product warranty: Modelling with 2D-renewal process. Int. J. Reliab. Saf. (2008)","DOI":"10.1504\/IJRS.2008.021065"},{"issue":"2","key":"55_CR9","doi-asserted-by":"publisher","first-page":"175","DOI":"10.3758\/BF03193146","volume":"39","author":"Franz Faul","year":"2007","unstructured":"Faul, F., Erdfelder, E., Lang, A.G., Buchner, A.: G* power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Res. Methods 39(2), 175\u2013191 (2007)","journal-title":"Behavior Research Methods"},{"issue":"1","key":"55_CR10","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1198\/004017006000000390","volume":"49","author":"M Fredette","year":"2007","unstructured":"Fredette, M., Lawless, J.F.: Finite-horizon prediction of recurrent events, with application to forecasts of warranty claims. Technometrics 49(1), 66\u201380 (2007)","journal-title":"Technometrics"},{"key":"55_CR11","doi-asserted-by":"crossref","unstructured":"Hira, Z.M., Gillies, D.F.: A review of feature selection and feature extraction methods applied on microarray data. Adv. Bioinform. 2015 (2015)","DOI":"10.1155\/2015\/198363"},{"issue":"3","key":"55_CR12","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1080\/00401706.1991.10484834","volume":"33","author":"J. D. Kalbfleisch","year":"1991","unstructured":"Kalbfleisch, J., Lawless, J., Robinson, J.: Methods for the analysis and prediction of warranty claims. Technometrics 33(3), 273\u2013285 (1991)","journal-title":"Technometrics"},{"key":"55_CR13","unstructured":"Kaminskiy, M.P., Krivtsov, V.V.: G-renewal process as a model for statistical warranty claim prediction. In: 2000 Proceedings Annual Reliability and Maintainability Symposium, International Symposium on Product Quality and Integrity (Cat. No. 00CH37055), pp. 276\u2013280. IEEE (2000)"},{"issue":"7","key":"55_CR14","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1108\/02656710510610820","volume":"22","author":"R Karim","year":"2005","unstructured":"Karim, R., Suzuki, K.: Analysis of warranty claim data: A literature review. Int. J. Qual. Reliab. Manage. 22(7), 667\u2013686 (2005)","journal-title":"Int. J. Qual. Reliab. Manage."},{"key":"55_CR15","doi-asserted-by":"crossref","unstructured":"Kleyner, A., Sanborn, K.: Modelling automotive warranty claims with build-to-sale data uncertainty. Int. J. Reliab. Saf. (2008)","DOI":"10.1504\/IJRS.2008.021063"},{"issue":"1","key":"55_CR16","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1111\/j.1751-5823.1998.tb00405.x","volume":"66","author":"J.F. Lawless","year":"1998","unstructured":"Lawless, J.: Statistical analysis of product warranty data. Int. Stat. Rev. 66(1), 41\u201360 (1998)","journal-title":"International Statistical Review"},{"key":"55_CR17","unstructured":"Nowaczyk, S., Prytz, R., R\u00f6gnvaldsson, T., Byttner, S.: Towards a machine learning algorithm for predicting truck compressor failures using logged vehicle data. In: 12th Scandinavian Conference on Artificial Intelligence, Aalborg, Denmark, November 20\u201322, 2013, pp. 205\u2013214. IOS Press (2013)"},{"key":"55_CR18","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.engappai.2015.02.009","volume":"41","author":"R Prytz","year":"2015","unstructured":"Prytz, R., Nowaczyk, S., R\u00f6gnvaldsson, T., Byttner, S.: Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Eng. Appl. Artif. Intell. 41, 139\u2013150 (2015)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"7","key":"55_CR19","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1080\/00207720500139930","volume":"36","author":"B. Rai","year":"2005","unstructured":"Rai, B., Singh, N.: Forecasting warranty performance in the presence of the \u2018maturing data\u2019 phenomenon. Int. J. Syst. Sci. 36(7) (2005)","journal-title":"International Journal of Systems Science"},{"issue":"2","key":"55_CR20","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1007\/s10618-017-0538-6","volume":"32","author":"T R\u00f6gnvaldsson","year":"2018","unstructured":"R\u00f6gnvaldsson, T., Nowaczyk, S., Byttner, S., Prytz, R., Svensson, M.: Self-monitoring for maintenance of vehicle fleets. Data Min. Knowl. Disc. 32(2), 344\u2013384 (2018)","journal-title":"Data Min. Knowl. Disc."},{"issue":"4","key":"55_CR21","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.1239\/aap\/1035228207","volume":"30","author":"ND Singpurwalla","year":"1998","unstructured":"Singpurwalla, N.D., Wilson, S.P.: Failure models indexed by two scales. Adv. Appl. Prob. 30(4), 1058\u20131072 (1998)","journal-title":"Adv. Appl. Prob."},{"key":"55_CR22","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1201\/9781420089653.ch10","volume":"9","author":"D Steinberg","year":"2009","unstructured":"Steinberg, D., Colla, P.: Cart: Classification and regression trees. Top Ten Algorithms Data Min. 9, 179 (2009)","journal-title":"Top Ten Algorithms Data Min."},{"issue":"11","key":"55_CR23","doi-asserted-by":"publisher","first-page":"3723","DOI":"10.1109\/TITS.2018.2865103","volume":"19","author":"E Vaiciukynas","year":"2018","unstructured":"Vaiciukynas, E., Ulicny, M., Pashami, S., Nowaczyk, S.: Learning low-dimensional representation of bivariate histogram data. IEEE Trans. Intell. Transp. Syst. 19(11), 3723\u20133735 (2018)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"1","key":"55_CR24","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0360-8352(92)90031-E","volume":"22","author":"GS Wasserman","year":"1992","unstructured":"Wasserman, G.S.: An application of dynamic linear models for predicting warranty claims. Comput. Ind. Eng. 22(1), 37\u201347 (1992)","journal-title":"Comput. Ind. Eng."},{"issue":"12","key":"55_CR25","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1080\/15458830.1996.11770751","volume":"28","author":"Gary S. Wasserman","year":"1996","unstructured":"Wasserman, G.S., Sudjianto, A.: A comparison of three strategies for forecasting warranty claims. IIE Trans., 967\u2013977 (1996)","journal-title":"IIE Transactions"},{"key":"55_CR26","unstructured":"Wasserman, G., Sudjianto, A.: Neural networks for forecasting warranty claims. Intell. Eng. Syst. Through Artif. Neural Netw. (2001)"},{"key":"55_CR27","unstructured":"Welch, G., Bishop, G., et al.: An Introduction to the Kalman Filter (1995)"},{"issue":"8","key":"55_CR28","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1002\/qre.1282","volume":"28","author":"S Wu","year":"2012","unstructured":"Wu, S.: Warranty data analysis: A review. Qual. Reliab. Eng. Int. 28(8), 795\u2013805 (2012)","journal-title":"Qual. Reliab. Eng. Int."},{"key":"55_CR29","unstructured":"Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: ICML, vol. 97, p. 35 (1997)"}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30241-2_55","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,31]],"date-time":"2019-08-31T09:32:58Z","timestamp":1567243978000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30241-2_55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030302405","9783030302412"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30241-2_55","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"30 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vila Real","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2019.utad.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"252","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":"119","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":"6","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":"47% - 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":"3.32","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":"1.86","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)"}}]}}