{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:41:17Z","timestamp":1743010877915,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031442223"},{"type":"electronic","value":"9783031442230"}],"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\/978-3-031-44223-0_19","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T05:01:41Z","timestamp":1695272501000},"page":"231-242","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MIPCE: Generating Multiple Patches Counterfactual-Changing Explanations for\u00a0Time Series Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7482-6871","authenticated-orcid":false,"given":"Hiroyuki","family":"Okumura","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2841-9538","authenticated-orcid":false,"given":"Tomoharu","family":"Nagao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Akula, A., Wang, S., Zhu, S.C.: Cocox: generating conceptual and counterfactual explanations via fault-lines. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 2594\u20132601 (2020)","key":"19_CR1","DOI":"10.1609\/aaai.v34i03.5643"},{"issue":"1","key":"19_CR2","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1214\/06-BA104","volume":"1","author":"DM Blei","year":"2006","unstructured":"Blei, D.M., Jordan, M.I.: Variational inference for Dirichlet process mixtures. Bayesian Anal. 1(1), 121\u2013143 (2006)","journal-title":"Bayesian Anal."},{"unstructured":"Bolei, Z., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Object detectors emerge in deep scene cnns. In: International Conference on Learning Representations (2015)","key":"19_CR3"},{"doi-asserted-by":"crossref","unstructured":"Byrne, R.M.: Counterfactuals in explainable artificial intelligence (xai): evidence from human reasoning. In: IJCAI, pp. 6276\u20136282 (2019)","key":"19_CR4","DOI":"10.24963\/ijcai.2019\/876"},{"doi-asserted-by":"crossref","unstructured":"Casella, G., Robert, C.P., Wells, M.T.: Generalized accept-reject sampling schemes. In: Lecture Notes-Monograph Series, pp. 342\u2013347 (2004)","key":"19_CR5","DOI":"10.1214\/lnms\/1196285403"},{"issue":"6","key":"19_CR6","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1109\/JAS.2019.1911747","volume":"6","author":"HA Dau","year":"2019","unstructured":"Dau, H.A., et al.: The ucr time series archive. IEEE\/CAA J. Automatica Sinica 6(6), 1293\u20131305 (2019)","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"19_CR7","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/978-3-030-86957-1_3","volume-title":"Case-Based Reasoning Research and Development","author":"E Delaney","year":"2021","unstructured":"Delaney, E., Greene, D., Keane, M.T.: Instance-based counterfactual explanations for time series classification. In: S\u00e1nchez-Ruiz, A.A., Floyd, M.W. (eds.) ICCBR 2021. LNCS (LNAI), vol. 12877, pp. 32\u201347. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86957-1_3"},{"key":"19_CR8","first-page":"1","volume":"31","author":"A Dhurandhar","year":"2018","unstructured":"Dhurandhar, A., et al.: Explanations based on the missing: towards contrastive explanations with pertinent negatives. Adv. Neural Inf. Process. Syst. 31, 1\u201312 (2018)","journal-title":"Adv. Neural Inf. Process. Syst."},{"doi-asserted-by":"crossref","unstructured":"Guidotti, R., Monreale, A., Spinnato, F., Pedreschi, D., Giannotti, F.: Explaining any time series classifier. In: 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI), pp. 167\u2013176 (2020)","key":"19_CR9","DOI":"10.1109\/CogMI50398.2020.00029"},{"issue":"4","key":"19_CR10","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","volume":"33","author":"H Ismail Fawaz","year":"2019","unstructured":"Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Deep learning for time series classification: a review. Data Min. Knowl. Disc. 33(4), 917\u2013963 (2019)","journal-title":"Data Min. Knowl. Disc."},{"unstructured":"Joshi, S., Koyejo, O., Vijitbenjaronk, W.D., Kim, B., Ghosh, J.: Towards realistic individual recourse and actionable explanations in black-box decision making systems. ArXiv arXiv:1907.09615v1 (2019)","key":"19_CR11"},{"doi-asserted-by":"crossref","unstructured":"Karlsson, I., Rebane, J., Papapetrou, P., Gionis, A.: Explainable time series tweaking via irreversible and reversible temporal transformations. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 207\u2013216. IEEE (2018)","key":"19_CR12","DOI":"10.1109\/ICDM.2018.00036"},{"doi-asserted-by":"crossref","unstructured":"Kenny, E.M., Keane, M.T.: On generating plausible counterfactual and semi-factual explanations for deep learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11575\u201311585 (2021)","key":"19_CR13","DOI":"10.1609\/aaai.v35i13.17377"},{"key":"19_CR14","first-page":"1","volume":"16","author":"N Lawrence","year":"2003","unstructured":"Lawrence, N.: Gaussian process latent variable models for visualisation of high dimensional data. Adv. Neural Inf. Process. Syst. 16, 1\u20138 (2003)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"19_CR15","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1007\/BF01589116","volume":"45","author":"DC Liu","year":"1989","unstructured":"Liu, D.C., Nocedal, J.: On the limited memory bfgs method for large scale optimization. Math. Program. 45(1), 503\u2013528 (1989)","journal-title":"Math. Program."},{"doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413\u2013422. IEEE (2008)","key":"19_CR16","DOI":"10.1109\/ICDM.2008.17"},{"doi-asserted-by":"crossref","unstructured":"Liu, S., Kailkhura, B., Loveland, D., Han, Y.: Generative counterfactual introspection for explainable deep learning. In: 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 1\u20135. IEEE (2019)","key":"19_CR17","DOI":"10.1109\/GlobalSIP45357.2019.8969491"},{"key":"19_CR18","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1007\/978-3-030-86520-7_40","volume-title":"Machine Learning and Knowledge Discovery in Databases. Research Track","author":"A Van Looveren","year":"2021","unstructured":"Van Looveren, A., Klaise, J.: Interpretable counterfactual explanations guided by prototypes. In: Oliver, N., P\u00e9rez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds.) ECML PKDD 2021. LNCS (LNAI), vol. 12976, pp. 650\u2013665. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86520-7_40"},{"key":"19_CR19","first-page":"1","volume":"30","author":"SM Lundberg","year":"2017","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 1\u201310 (2017)","journal-title":"Adv. Neural Inf. Process. Syst."},{"doi-asserted-by":"crossref","unstructured":"Mark T Keane, Eoin M Kenny, E.D., Smyth, B.: If only we had better counterfactual explanations: five key deficits to rectify in the evaluation of counterfactual xai techniques. In: Proceeding of the 30th International Joint Conference on Artificial Intelligence, IJCAI, pp. 4466\u20134474 (2021)","key":"19_CR20","DOI":"10.24963\/ijcai.2021\/609"},{"doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cwhy should i trust you?\u201d explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","key":"19_CR21","DOI":"10.1145\/2939672.2939778"},{"issue":"7","key":"19_CR22","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., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443\u20131471 (2001)","journal-title":"Neural Comput."},{"unstructured":"Singla, S., Pollack, B., Chen, J., Batmanghelich, K.: Explanation by progressive exaggeration. In: International Conference on Learning Representations (2020)","key":"19_CR23"},{"unstructured":"Titsias, M., Lawrence, N.D.: Bayesian gaussian process latent variable model. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 844\u2013851. JMLR Workshop and Conference Proceedings (2010)","key":"19_CR24"},{"key":"19_CR25","first-page":"841","volume":"31","author":"S Wachter","year":"2017","unstructured":"Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: automated decisions and the gdpr. Harv. JL Tech. 31, 841 (2017)","journal-title":"Harv. JL Tech."},{"doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1578\u20131585. IEEE (2017)","key":"19_CR26","DOI":"10.1109\/IJCNN.2017.7966039"},{"issue":"1","key":"19_CR27","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s10618-010-0179-5","volume":"22","author":"L Ye","year":"2011","unstructured":"Ye, L., Keogh, E.: Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min. Knowl. Disc. 22(1), 149\u2013182 (2011)","journal-title":"Data Min. Knowl. Disc."},{"doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","key":"19_CR28","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44223-0_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T06:13:43Z","timestamp":1695276823000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44223-0_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442223","9783031442230"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44223-0_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","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":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"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":"type of other papers accepted  : 9 Abstract","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)"}}]}}