{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:17:30Z","timestamp":1742912250307,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031236174"},{"type":"electronic","value":"9783031236181"}],"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\/978-3-031-23618-1_26","type":"book-chapter","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T07:05:49Z","timestamp":1675062349000},"page":"385-401","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable and\u00a0Reliable Rule Classification Based on\u00a0Conformal Prediction"],"prefix":"10.1007","author":[{"given":"Husam","family":"Abdelqader","sequence":"first","affiliation":[]},{"given":"Evgueni","family":"Smirnov","sequence":"additional","affiliation":[]},{"given":"Marc","family":"Pont","sequence":"additional","affiliation":[]},{"given":"Marciano","family":"Geijselaers","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"26_CR1","unstructured":"Bostr\u00f6m, H., Johansson, U.: Mondrian conformal regressors. In: Proceedings of the 9th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2020. Proceedings of Machine Learning Research, vol. 128, pp. 114\u2013133. PMLR (2020)"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Cohen, W.W.: Fast Effective Rule Induction. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 115\u2013123. Morgan Kaufmann (1995)","DOI":"10.1016\/B978-1-55860-377-6.50023-2"},{"key":"26_CR3","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2017) http:\/\/archive.ics.uci.edu\/ml"},{"key":"26_CR4","doi-asserted-by":"publisher","unstructured":"Furnkranz, J., Gamberger, D., Lavrac, N.: Foundations of Rule Learning. Springer, Berlin (2012). https:\/\/doi.org\/10.1007\/978-3-540-75197-7","DOI":"10.1007\/978-3-540-75197-7"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Johansson, U., Linusson, H., L\u00f6fstr\u00f6m, T., Bostr\u00f6m, H.: Conformal prediction using decision trees. In: Proceedings of the 13th IEEE International Conference on Data Mining, pp. 330\u2013339. IEEE Computer Society (2013)","DOI":"10.1109\/ICDM.2013.85"},{"key":"26_CR6","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.eswa.2017.12.041","volume":"97","author":"U Johansson","year":"2018","unstructured":"Johansson, U., Linusson, H., L\u00f6fstr\u00f6m, T., Bostr\u00f6m, H.: Interpretable regression trees using conformal prediction. Expert Syst. Appl. 97, 394\u2013404 (2018)","journal-title":"Expert Syst. Appl."},{"key":"26_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2022.108554","volume":"126","author":"U Johansson","year":"2022","unstructured":"Johansson, U., S\u00f6nstr\u00f6d, C., L\u00f6fstr\u00f6m, T., Bostr\u00f6m, H.: Rule extraction with guarantees from regression models. Pattern Recogn. 126, 1\u20139 (2022)","journal-title":"Pattern Recogn."},{"key":"26_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","volume":"267","author":"T Miller","year":"2019","unstructured":"Miller, T.: Explanation in artificial intelligence: Insights from the social sciences. J. Artif. Intell. 267, 1\u201338 (2019)","journal-title":"J. Artif. Intell."},{"key":"26_CR9","unstructured":"Molnar, C.: Interpretable Machine learning: a guide for making black Box Models Explainable (2022)"},{"key":"26_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/3-540-36755-1_29","volume-title":"Machine Learning","author":"H Papadopoulos","year":"2002","unstructured":"Papadopoulos, H., Proedrou, K., Vovk, V., Gammerman, A.: Inductive confidence machines for regression. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 345\u2013356. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-36755-1_29"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"van Prehn, J., Smirnov, E.N.: Region classification with decision trees. In: Proceedings of the 8th IEEE International Conference on Data Mining Workshops, pp. 53\u201359 (2008)","DOI":"10.1109\/ICDMW.2008.19"},{"key":"26_CR12","unstructured":"Shafer, G., Vovk, V.: A tutorial on conformal prediction. arXiv:0706.3188 [cs, stat], June 2007"},{"key":"26_CR13","doi-asserted-by":"publisher","first-page":"108507","DOI":"10.1016\/j.patcog.2021.108507","volume":"124","author":"P Toccaceli","year":"2022","unstructured":"Toccaceli, P.: Introduction to conformal predictors. Pattern Recogn. 124, 108507 (2022)","journal-title":"Pattern Recogn."},{"key":"26_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/b106715","volume-title":"Algorithmic Learning in a Random World","author":"V Vovk","year":"2005","unstructured":"Vovk, V., Gammerman, A., Shafer, G.: Algorithmic Learning in a Random World. Springer, New York (2005). https:\/\/doi.org\/10.1007\/b106715"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-23618-1_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T07:13:46Z","timestamp":1675062826000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23618-1_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031236174","9783031236181"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23618-1_26","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":"31 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.ecmlpkdd.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1060","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":"236","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":"3-4","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":"3-4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17 demo track papers have been accepted from 28 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}