{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:38:30Z","timestamp":1743043110422,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031165634"},{"type":"electronic","value":"9783031165641"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16564-1_16","type":"book-chapter","created":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T23:02:43Z","timestamp":1664146963000},"page":"162-174","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A General-Purpose Method for\u00a0Applying Explainable AI for\u00a0Anomaly Detection"],"prefix":"10.1007","author":[{"given":"John","family":"Sipple","sequence":"first","affiliation":[]},{"given":"Abdou","family":"Youssef","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,26]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (xai). IEEE Access 6, 52138\u201352160 (2018)","journal-title":"IEEE Access"},{"key":"16_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47578-3","volume-title":"Outlier Analysis","author":"CC Aggarwal","year":"2016","unstructured":"Aggarwal, C.C.: Outlier Analysis, 2nd edn. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-47578-3","edition":"2"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Antwarg, L., Miller, R.M., Shapira, B., Rokach, L.: Explaining anomalies detected by autoencoders using shap (2020)","DOI":"10.1016\/j.eswa.2021.115736"},{"issue":"7","key":"16_CR4","first-page":"1","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7), 1\u201346 (2015)","journal-title":"PLoS ONE"},{"key":"16_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-319-44781-0_8","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2016","author":"A Binder","year":"2016","unstructured":"Binder, A., Montavon, G., Lapuschkin, S., M\u00fcller, K.-R., Samek, W.: Layer-wise relevance propagation for neural networks with local renormalization layers. In: Villa, A.E.P., Masulli, P., Pons Rivero, A.J. (eds.) ICANN 2016. LNCS, vol. 9887, pp. 63\u201371. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-44781-0_8"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Broniatowski, D.: Psychological foundations of explainability and interpretability in artificial intelligence (2021\u201304-12 04:04:00 2021)","DOI":"10.6028\/NIST.IR.8367"},{"key":"16_CR7","unstructured":"Carletti, M., Terzi, M., Susto, G.A.: Interpretable anomaly detection with diffi: Depth-based isolation forest feature importance (2020)"},{"key":"16_CR8","doi-asserted-by":"publisher","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3) (2009). https:\/\/doi.org\/10.1145\/1541880.1541882","DOI":"10.1145\/1541880.1541882"},{"key":"16_CR9","unstructured":"Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning (2017)"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M.A., Kagal, L.: Explaining explanations: an approach to evaluating interpretability of machine learning. CoRR abs\/1806.00069 (2018)","DOI":"10.1109\/DSAA.2018.00018"},{"key":"16_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107198","volume":"101","author":"J Kauffmann","year":"2020","unstructured":"Kauffmann, J., M\u00fcller, K.R., Montavon, G.: Towards explaining anomalies: a deep Taylor decomposition of one-class models. Pattern Recogn. 101, 107198 (2020). https:\/\/doi.org\/10.1016\/j.patcog.2020.107198","journal-title":"Pattern Recogn."},{"issue":"2","key":"16_CR12","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1080\/0952813X.2016.1198934","volume":"29","author":"A Lieto","year":"2017","unstructured":"Lieto, A., Radicioni, D.P., Rho, V.: Dual PECCS: a cognitive system for conceptual representation and categorization. J. Exp. Theor. Artif. Intell. 29(2), 433\u2013452 (2017)","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"16_CR13","unstructured":"Lipton, Z.C.: The mythos of model interpretability. CoRR abs\/1606.03490 (2016). http:\/\/arxiv.org\/abs\/1606.03490"},{"key":"16_CR14","doi-asserted-by":"publisher","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413\u2013422, December 2008. https:\/\/doi.org\/10.1109\/ICDM.2008.17","DOI":"10.1109\/ICDM.2008.17"},{"key":"16_CR15","unstructured":"Liznerski, P., Ruff, L., Vandermeulen, R.A., Franks, B.J., Kloft, M., M\u00fcller, K.R.: Explainable deep one-class classification (2021)"},{"key":"16_CR16","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)"},{"key":"16_CR17","unstructured":"Merrick, L., Taly, A.: The explanation game: explaining machine learning models with cooperative game theory. CoRR abs\/1909.08128 (2019)"},{"key":"16_CR18","doi-asserted-by":"publisher","unstructured":"Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artificial Intelligence 267 (2017). https:\/\/doi.org\/10.1016\/j.artint.2018.07.007","DOI":"10.1016\/j.artint.2018.07.007"},{"key":"16_CR19","doi-asserted-by":"publisher","DOI":"10.1017\/S0269888921000102","volume":"36","author":"T Miller","year":"2021","unstructured":"Miller, T.: Contrastive explanation: a structural-model approach. Knowl. Eng. Rev. 36, e14 (2021). https:\/\/doi.org\/10.1017\/S0269888921000102","journal-title":"Knowl. Eng. Rev."},{"key":"16_CR20","unstructured":"Montavon, G., Bach, S., Binder, A., Samek, W., M\u00fcller, K.: Explaining nonlinear classification decisions with deep Taylor decomposition. CoRR abs\/1512.02479 (2015)"},{"issue":"2","key":"16_CR21","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.jmp.2006.11.003","volume":"51","author":"DJ Navarro","year":"2007","unstructured":"Navarro, D.J.: On the interaction between exemplar-based concepts and a response scaling process. Math. Psychol. 51(2), 85\u201398 (2007)","journal-title":"Math. Psychol."},{"issue":"1","key":"16_CR22","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1037\/0096-3445.115.1.39","volume":"115","author":"RM Nosofsky","year":"1986","unstructured":"Nosofsky, R.M.: Attention, similarity, and the identification-categorization relationship. J. Exp. Psychol. 115(1), 39\u201361 (1986)","journal-title":"J. Exp. Psychol."},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cwhy should I trust you?\u201d: explaining the predictions of any classifier. CoRR abs\/1602.04938 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"16_CR24","volume-title":"Interpretation Theory: Discourse and the Surplus of Meaning","author":"P Ricoeur","year":"1972","unstructured":"Ricoeur, P.: Interpretation Theory: Discourse and the Surplus of Meaning. Texas Christian University Press, Fort Worth (1972)"},{"key":"16_CR25","unstructured":"Ruff, L., et al.: A unifying review of deep and shallow anomaly detection. CoRR abs\/2009.11732 (2020)"},{"key":"16_CR26","unstructured":"Samek, W., Wiegand, T., M\u00fcller, K.: Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. CoRR abs\/1708.08296 (2017)"},{"issue":"7","key":"16_CR27","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1162\/089976601750264965","volume":"13","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf, B., Platt, J.C., Shawe-Taylor, J.C., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443\u20131471 (2001). https:\/\/doi.org\/10.1162\/089976601750264965","journal-title":"Neural Comput."},{"key":"16_CR28","volume-title":"The Mathematical Theory of Communication","author":"CE Shannon","year":"1949","unstructured":"Shannon, C.E., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press, Urbana (1949)"},{"key":"16_CR29","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/BF02288967","volume":"22","author":"RN Shepard","year":"1957","unstructured":"Shepard, R.N.: Stimulus and response generalization: a stochastic model relating generalization to distance in psychological space. PsychometrikaR 22, 325\u2013345 (1957)","journal-title":"PsychometrikaR"},{"issue":"4820","key":"16_CR30","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1126\/science.3629243","volume":"237","author":"RN Shepard","year":"1987","unstructured":"Shepard, R.N.: Toward a universal law of generalization for psychological science. Science 237(4820), 1317\u20131323 (1987). https:\/\/doi.org\/10.1126\/science.3629243","journal-title":"Science"},{"key":"16_CR31","unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences (2017)"},{"key":"16_CR32","unstructured":"Sipple, J.: Interpretable, multidimensional, multimodal anomaly detection with negative sampling for detection of device failure. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, pp. 9016\u20139025. PMLR, 13\u201318 July 2020"},{"key":"16_CR33","doi-asserted-by":"crossref","unstructured":"Sipple, J., Youssef, A.: A general-purpose method for applying Explainable AI for anomaly detection (extended manuscript) (2022). https:\/\/arxiv.org\/abs\/2207.11564","DOI":"10.1007\/978-3-031-16564-1_16"},{"key":"16_CR34","unstructured":"Sundararajan, M., Najmi, A.: The many Shapley values for model explanation. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, pp. 9269\u20139278. PMLR, 13\u201318 July 2020"},{"key":"16_CR35","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. CoRR abs\/1703.01365 (2017)"},{"key":"16_CR36","first-page":"155","volume":"2","author":"DMJ Tax","year":"2002","unstructured":"Tax, D.M.J., Duin, R.P.W.: Uniform object generation for optimizing one-class classifiers. J. Mach. Learn. Res. 2, 155\u2013173 (2002)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Foundations of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16564-1_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T14:46:53Z","timestamp":1710341213000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16564-1_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031165634","9783031165641"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16564-1_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"26 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISMIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Methodologies for Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cosenza","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"3 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ismis2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ismis2022.icar.cnr.it\/","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":"71","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":"31","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":"11","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":"44% - 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":"3.7","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","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)"}},{"value":"Number and type of other papers accepted :\t4 industrial papers","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)"}}]}}