{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:12:53Z","timestamp":1742955173775,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031429408"},{"type":"electronic","value":"9783031429415"}],"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\/978-3-031-42941-5_56","type":"book-chapter","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T08:02:46Z","timestamp":1693382566000},"page":"631-638","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Towards Reliable Machine Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7386-2512","authenticated-orcid":false,"given":"Simona","family":"Nistic\u00f2","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"issue":"1","key":"56_CR1","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1007\/s10618-016-0458-x","volume":"31","author":"F Angiulli","year":"2017","unstructured":"Angiulli, F., Fassetti, F., Manco, G., Palopoli, L.: Outlying property detection with numerical attributes. Data Min. Knowl. Disc. 31(1), 134\u2013163 (2017)","journal-title":"Data Min. Knowl. Disc."},{"key":"56_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/978-3-030-91608-4_46","volume-title":"Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2021","author":"F Angiulli","year":"2021","unstructured":"Angiulli, F., Fassetti, F., Nistic\u00f2, S.: Finding local explanations through masking models. In: Yin, H., et al. (eds.) IDEAL 2021. LNCS, vol. 13113, pp. 467\u2013475. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-91608-4_46"},{"key":"56_CR3","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/978-3-030-88942-5_31","volume-title":"Discovery Science","author":"F Angiulli","year":"2021","unstructured":"Angiulli, F., Fassetti, F., Nistic\u00f2, S.: Local interpretable classifier explanations with self-generated semantic features. In: Soares, C., Torgo, L. (eds.) DS 2021. LNCS (LNAI), vol. 12986, pp. 401\u2013410. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-88942-5_31"},{"doi-asserted-by":"publisher","unstructured":"Angiulli, F., Fassetti, F., Nistic\u00f2, S., Palopoli, L.: Outlier explanation through masking models. In: Advances in Databases and Information Systems: 26th European Conference, ADBIS 2022, Turin, Italy, 5\u20138 September 2022, Proceedings, pp. 392\u2013406. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-15740-0_28","key":"56_CR4","DOI":"10.1007\/978-3-031-15740-0_28"},{"unstructured":"Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., Su, J.K.: This looks like that: deep learning for interpretable image recognition. In: Advances in Neural Information Processing Systems 32 (2019)","key":"56_CR5"},{"issue":"12","key":"56_CR6","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1038\/s42256-020-00265-z","volume":"2","author":"Z Chen","year":"2020","unstructured":"Chen, Z., Bei, Y., Rudin, C.: Concept whitening for interpretable image recognition. Nat. Mach. Intell. 2(12), 772\u2013782 (2020)","journal-title":"Nat. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Dang, X.H., Assent, I., Ng, R.T., Zimek, A., Schubert, E.: Discriminative features for identifying and interpreting outliers. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 88\u201399. IEEE (2014)","key":"56_CR7","DOI":"10.1109\/ICDE.2014.6816642"},{"issue":"5","key":"56_CR8","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1007\/s10618-014-0398-2","volume":"29","author":"L Duan","year":"2015","unstructured":"Duan, L., Tang, G., Pei, J., Bailey, J., Campbell, A., Tang, C.: Mining outlying aspects on numeric data. Data Min. Knowl. Disc. 29(5), 1116\u20131151 (2015)","journal-title":"Data Min. Knowl. Disc."},{"issue":"6","key":"56_CR9","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/MIS.2019.2957223","volume":"34","author":"R Guidotti","year":"2019","unstructured":"Guidotti, R., Monreale, A., Giannotti, F., Pedreschi, D., Ruggieri, S., Turini, F.: Factual and counterfactual explanations for black box decision making. IEEE Intell. Syst. 34(6), 14\u201323 (2019)","journal-title":"IEEE Intell. Syst."},{"doi-asserted-by":"publisher","unstructured":"Hamon, R., Junklewitz, H., Malgieri, G., Hert, P.D., Beslay, L., Sanchez, I.: Impossible explanations? beyond explainable ai in the gdpr from a Covid-19 use case scenario.In: FAccT 2021, pp. 549\u2013559. ACM, New York (2021). https:\/\/doi.org\/10.1145\/3442188.3445917","key":"56_CR10","DOI":"10.1145\/3442188.3445917"},{"key":"56_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1007\/978-3-642-01307-2_86","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"H-P Kriegel","year":"2009","unstructured":"Kriegel, H.-P., Kr\u00f6ger, P., Schubert, E., Zimek, A.: Outlier detection in axis-parallel subspaces of high dimensional data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 831\u2013838. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-01307-2_86"},{"unstructured":"LeCun, Y.: The mnist database of handwritten digits (1998). http:\/\/yann.lecun.com\/exdb\/mnist\/","key":"56_CR12"},{"doi-asserted-by":"crossref","unstructured":"Liu, N., Shin, D., Hu, X.: Contextual outlier interpretation. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018, pp. 2461\u20132467. AAAI Press (2018)","key":"56_CR13","DOI":"10.24963\/ijcai.2018\/341"},{"unstructured":"Lundberg, S., Lee, S.I.: A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874 (2017)","key":"56_CR14"},{"unstructured":"Mnih, V., Heess, N., Graves, A., et al.: Recurrent models of visual attention. In: Advances in Neural Information Processing Systems 27 (2014)","key":"56_CR15"},{"doi-asserted-by":"crossref","unstructured":"Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 ACM FAccT, pp. 607\u2013617 (2020)","key":"56_CR16","DOI":"10.1145\/3351095.3372850"},{"unstructured":"Petsiuk, V., Das, A., Saenko, K.: Rise: randomized input sampling for explanation of black-box models. arXiv preprint arXiv:1806.07421 (2018)","key":"56_CR17"},{"doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \" Why should i trust you?\" explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD KDD, pp. 1135\u20131144 (2016)","key":"56_CR18","DOI":"10.1145\/2939672.2939778"},{"doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Anchors: high-precision model-agnostic explanations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","key":"56_CR19","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"56_CR20","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1016\/j.patrec.2020.04.004","volume":"133","author":"I Rio-Torto","year":"2020","unstructured":"Rio-Torto, I., Fernandes, K., Teixeira, L.F.: Understanding the decisions of CNNS: an in-model approach. Pattern Recogn. Lett. 133, 373\u2013380 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"56_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1007\/978-3-030-62008-0_32","volume-title":"Web Information Systems Engineering \u2013 WISE 2020","author":"D Samariya","year":"2020","unstructured":"Samariya, D., Aryal, S., Ting, K.M., Ma, J.: A new effective and efficient measure for outlying aspect mining. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds.) WISE 2020. LNCS, vol. 12343, pp. 463\u2013474. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-62008-0_32"},{"doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE ICCV, pp. 618\u2013626 (2017)","key":"56_CR22","DOI":"10.1109\/ICCV.2017.74"},{"unstructured":"Shen, T., Mueller, J., Barzilay, R., Jaakkola, T.: Educating text autoencoders: latent representation guidance via denoising. In: ICML, pp. 8719\u20138729. PMLR (2020)","key":"56_CR23"},{"unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Visualising image classification models and saliency maps. In: Deep Inside Convolutional Networks (2014)","key":"56_CR24"},{"issue":"6","key":"56_CR25","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.1007\/s10618-016-0453-2","volume":"30","author":"NX Vinh","year":"2016","unstructured":"Vinh, N.X., et al.: Discovering outlying aspects in large datasets. Data Min. Knowl. Disc. 30(6), 1520\u20131555 (2016)","journal-title":"Data Min. Knowl. Disc."}],"container-title":["Communications in Computer and Information Science","New Trends in Database and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-42941-5_56","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,27]],"date-time":"2024-10-27T02:57:41Z","timestamp":1729997861000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-42941-5_56"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031429408","9783031429415"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-42941-5_56","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 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADBIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Advances in Databases and Information Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Barcelona","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"4 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adbis2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/adbis.eu\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"77","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":"14","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":"25","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":"18% - 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":"N\/A","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":"N\/A","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)"}}]}}