{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:01:40Z","timestamp":1772906500947,"version":"3.50.1"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031703409","type":"print"},{"value":"9783031703416","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-70341-6_22","type":"book-chapter","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T20:26:39Z","timestamp":1725049599000},"page":"367-386","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Data-Agnostic Pivotal Instances Selection for\u00a0Decision-Making Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5043-5942","authenticated-orcid":false,"given":"Alessio","family":"Cascione","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8351-9999","authenticated-orcid":false,"given":"Mattia","family":"Setzu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2827-7613","authenticated-orcid":false,"given":"Riccardo","family":"Guidotti","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"22_CR1","unstructured":"Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I.J., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: NeurIPS, pp. 9525\u20139536 (2018)"},{"key":"22_CR2","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.neunet.2020.07.010","volume":"130","author":"PP Angelov","year":"2020","unstructured":"Angelov, P.P., Soares, E.A.: Towards explainable deep neural networks (xDNN). Neural Netw. 130, 185\u2013194 (2020)","journal-title":"Neural Netw."},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Bertsimas, D., Dunn, J.: Optimal classification trees. MACH (2017)","DOI":"10.1007\/s10994-017-5633-9"},{"issue":"2","key":"22_CR4","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.artmed.2005.10.008","volume":"36","author":"I Bichindaritz","year":"2006","unstructured":"Bichindaritz, I., Marling, C.: Case-based reasoning in the health sciences: What\u2019s next? Artif. Intell. Medicine 36(2), 127\u2013135 (2006)","journal-title":"Artif. Intell. Medicine"},{"issue":"495","key":"22_CR5","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1198\/jasa.2011.tm10183","volume":"106","author":"J Bien","year":"2011","unstructured":"Bien, J., Tibshirani, R.: Hierarchical clustering with prototypes via minimax linkage. J. Am. Stat. Assoc. 106(495), 1075\u20131084 (2011)","journal-title":"J. Am. Stat. Assoc."},{"key":"22_CR6","doi-asserted-by":"publisher","first-page":"2403","DOI":"10.1214\/11-AOAS495","volume":"5","author":"J Bien","year":"2011","unstructured":"Bien, J., Tibshirani, R.: Prototype selection for interpretable classification. Ann. Appl. Stat. 5, 2403\u20132424 (2011)","journal-title":"Ann. Appl. Stat."},{"issue":"5","key":"22_CR7","first-page":"1719","volume":"37","author":"F Bodria","year":"2023","unstructured":"Bodria, F., Giannotti, F., et al.: Benchmarking and survey of explanation methods for black box models. DMKD 37(5), 1719\u20131778 (2023)","journal-title":"DMKD"},{"key":"22_CR8","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, Monterey (1984)"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Chatzakou, D., Leontiadis, I., et\u00a0al.: Detecting cyberbullying and cyberaggression in social media. ACM Trans. Web 13(3), 17:1\u201317:51 (2019)","DOI":"10.1145\/3343484"},{"key":"22_CR10","unstructured":"Chen, C., et\u00a0al.: This looks like that: deep learning for interpretable image recognition. In: NeurIPS, pp. 8928\u20138939 (2019)"},{"key":"22_CR11","unstructured":"Chui, M., Hall, B., Mayhew, H., Singla, A., Sukharevsky, A., McKinsey, A.: The State of AI in 2022-and a Half Decade in Review. Mc Kinsey, New York (2022)"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Das, A., et\u00a0al.: ProtoTex: explaining model decisions with prototype tensors. In: ACL (1), pp. 2986\u20132997. Association for Computational Linguistics (2022)","DOI":"10.18653\/v1\/2022.acl-long.213"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Davoodi, O., et\u00a0al.: On the interpretability of part-prototype based classifiers: a human centric analysis. CoRR abs\/2310.06966 (2023)","DOI":"10.1038\/s41598-023-49854-z"},{"issue":"9","key":"22_CR14","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","volume":"24","author":"J De Fauw","year":"2018","unstructured":"De Fauw, J., et al.: Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24(9), 1342\u20131350 (2018)","journal-title":"Nat. Med."},{"issue":"5","key":"22_CR15","first-page":"1454","volume":"34","author":"A Dempster","year":"2020","unstructured":"Dempster, A., et al.: ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels. DMKD 34(5), 1454\u20131495 (2020)","journal-title":"DMKD"},{"key":"22_CR16","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. JMLR 7, 1\u201330 (2006)","journal-title":"JMLR"},{"key":"22_CR17","unstructured":"Fix, E.: Discriminatory analysis: nonparametric discrimination, consistency properties, vol.\u00a01. USAF school of Aviation Medicine (1985)"},{"key":"22_CR18","unstructured":"Frosst, N., Hinton, G.E.: Distilling a neural network into a soft decision tree. In: CEx@AI*IA. CEUR Workshop Proceedings, vol.\u00a02071. CEUR-WS.org (2017)"},{"issue":"2","key":"22_CR19","first-page":"85","volume":"16","author":"AR Golding","year":"1995","unstructured":"Golding, A.R.: A review of case-based reasoning. AI Mag. 16(2), 85\u201386 (1995)","journal-title":"AI Mag."},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Guidotti, R., Monreale, A., et\u00a0al.: A survey of methods for explaining black box models. ACM CSUR 51(5), 93:1\u201393:42 (2019)","DOI":"10.1145\/3236009"},{"issue":"11","key":"22_CR21","first-page":"2151","volume":"31","author":"R Guidotti","year":"2019","unstructured":"Guidotti, R., Rossetti, G., et al.: Personalized market basket prediction with temporal annotated recurring sequences. IEEE TKDE 31(11), 2151\u20132163 (2019)","journal-title":"IEEE TKDE"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Hase, P., Chen, C., Li, O., Rudin, C.: Interpretable image recognition with hierarchical prototypes. In: HCOMP, pp. 32\u201340. AAAI Press (2019)","DOI":"10.1609\/hcomp.v7i1.5265"},{"key":"22_CR23","unstructured":"Hollmann, N., M\u00fcller, S., Eggensperger, K., Hutter, F.: TabPFN: a transformer that solves small tabular classification problems in a second. In: ICLR (2023)"},{"issue":"264","key":"22_CR24","first-page":"1","volume":"24","author":"D Hong","year":"2023","unstructured":"Hong, D., Wang, T., Baek, S.: Protorynet-interpretable text classification via prototype trajectories. JMLR 24(264), 1\u201339 (2023)","journal-title":"JMLR"},{"key":"22_CR25","unstructured":"Jeyakumar, J.V., et\u00a0al.: How can I explain this to you? An empirical study of deep neural network explanation methods. In: NeurIPS (2020)"},{"issue":"43","key":"22_CR26","doi-asserted-by":"publisher","first-page":"18243","DOI":"10.1073\/pnas.1012933107","volume":"107","author":"PN Johnson-Laird","year":"2010","unstructured":"Johnson-Laird, P.N.: Mental models and human reasoning. Proc. Natl. Acad. Sci. 107(43), 18243\u201318250 (2010)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"22_CR27","doi-asserted-by":"crossref","unstructured":"Kasirzadeh, A., Clifford, D.: Fairness and data protection impact assessments. In: AIES, pp. 146\u2013153. ACM (2021)","DOI":"10.1145\/3461702.3462528"},{"key":"22_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/978-3-031-19775-8_17","volume-title":"ECCV 2022","author":"SSY Kim","year":"2022","unstructured":"Kim, S.S.Y., et al.: HIVE: evaluating the human interpretability of visual explanations. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13672, pp. 280\u2013298. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19775-8_17"},{"issue":"22","key":"22_CR29","doi-asserted-by":"publisher","first-page":"4893","DOI":"10.1016\/j.ins.2007.05.027","volume":"177","author":"T Korenius","year":"2007","unstructured":"Korenius, T., Laurikkala, J., Juhola, M.: On principal component analysis, cosine and euclidean measures in information retrieval. Inf. Sci. 177(22), 4893\u20134905 (2007)","journal-title":"Inf. Sci."},{"key":"22_CR30","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1007\/978-3-031-30047-9_19","volume-title":"IDA 223","author":"C Landi","year":"2023","unstructured":"Landi, C., et al.: Geolet: an interpretable model for trajectory classification. In: Cr\u00e9milleux, B., Hess, S., Nijssen, S. (eds.) IDA 223. LNCS, vol. 13876, pp. 236\u2013248. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-30047-9_19"},{"issue":"12","key":"22_CR31","doi-asserted-by":"publisher","first-page":"2257","DOI":"10.1080\/14697688.2022.2118071","volume":"22","author":"W Li","year":"2022","unstructured":"Li, W., et al.: A data-driven explainable case-based reasoning approach for financial risk detection. Quant. Finance 22(12), 2257\u20132274 (2022)","journal-title":"Quant. Finance"},{"issue":"3","key":"22_CR32","first-page":"607","volume":"33","author":"B Lucas","year":"2019","unstructured":"Lucas, B., Shifaz, A., et al.: Proximity forest: an effective and scalable distance-based classifier for time series. DMKD 33(3), 607\u2013635 (2019)","journal-title":"DMKD"},{"key":"22_CR33","doi-asserted-by":"crossref","unstructured":"Ming, Y., et\u00a0al.: Interpretable and steerable sequence learning via prototypes. In: KDD, pp. 903\u2013913. ACM (2019)","DOI":"10.1145\/3292500.3330908"},{"key":"22_CR34","doi-asserted-by":"crossref","unstructured":"Naretto, F., Monreale, A., Giannotti, F.: Evaluating the privacy exposure of interpretable global explainers. In: CogMI, pp. 13\u201319. IEEE (2022)","DOI":"10.1109\/CogMI56440.2022.00012"},{"key":"22_CR35","doi-asserted-by":"crossref","unstructured":"Nauta, M., van Bree, R., Seifert, C.: Neural prototype trees for interpretable fine-grained image recognition. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01469"},{"key":"22_CR36","unstructured":"Nguyen, G., et\u00a0al.: The effectiveness of feature attribution methods and its correlation with automatic evaluation scores. In: NeurIPS, pp. 26422\u201326436 (2021)"},{"key":"22_CR37","doi-asserted-by":"crossref","unstructured":"Pekalska, E., Duin, R.P.W.: The Dissimilarity Representation for Pattern Recognition, Series in MPAI, vol.\u00a064. WorldScientific (2005)","DOI":"10.1142\/9789812703170"},{"key":"22_CR38","unstructured":"Schank, R.C., Abelson, R.P.: Knowledge and Memory: The Real Story. In: Knowledge and Memory: The Real Story, pp. 1\u201385. Psychology Press (2014)"},{"key":"22_CR39","doi-asserted-by":"crossref","unstructured":"Spelke, E.S.: What Babies Know: Core Knowledge and Composition, vol. 1. Oxford University Press, New York (2022)","DOI":"10.1093\/oso\/9780190618247.001.0001"},{"key":"22_CR40","volume-title":"Data Mining Introduction","author":"PN Tan","year":"2006","unstructured":"Tan, P.N., Steinbach, M., Kumar, V.: Data Mining Introduction. People\u2019s Posts and Telecommunications Publishing House, Beijing (2006)"},{"key":"22_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2020.103404","volume":"291","author":"JVD Waa","year":"2021","unstructured":"Waa, J.V.D., et al.: Evaluating XAI: a comparison of rule-based and example-based explanations. Artif. Intell. 291, 103404 (2021)","journal-title":"Artif. Intell."},{"issue":"8","key":"22_CR42","doi-asserted-by":"publisher","first-page":"2655","DOI":"10.3390\/s24082655","volume":"24","author":"J Xie","year":"2024","unstructured":"Xie, J., et al.: Prototype learning for medical time series classification via human-machine collaboration. Sensors 24(8), 2655 (2024)","journal-title":"Sensors"},{"key":"22_CR43","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.inffus.2021.07.016","volume":"77","author":"G Yang","year":"2022","unstructured":"Yang, G., et al.: Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion. Inf. Fusion 77, 29\u201352 (2022)","journal-title":"Inf. Fusion"},{"key":"22_CR44","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Yang, Y., Ma, H., Wu, Y.N.: Interpreting CNNs via Decision Trees. In: CVPR, pp. 6261\u20136270. Computer Vision Foundation \/ IEEE (2019)","DOI":"10.1109\/CVPR.2019.00642"},{"issue":"8","key":"22_CR45","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.4304\/jsw.7.8.1775-1782","volume":"7","author":"X Zhang","year":"2012","unstructured":"Zhang, X., Jiang, S.: A splitting criteria based on similarity in decision tree learning. J. Softw. 7(8), 1775\u20131782 (2012)","journal-title":"J. Softw."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70341-6_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T20:30:43Z","timestamp":1725049843000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70341-6_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031703409","9783031703416"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70341-6_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}