{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:09:44Z","timestamp":1765544984574,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,21]]},"DOI":"10.1145\/3627673.3679952","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:34:11Z","timestamp":1729452851000},"page":"4158-4162","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Learning Counterfactual Explanations with Intervals for Time-series Classification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6302-8989","authenticated-orcid":false,"given":"Akihiro","family":"Yamaguchi","sequence":"first","affiliation":[{"name":"Corporate R&amp;D Center, Toshiba Corporation, Kawasaki, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5580-2578","authenticated-orcid":false,"given":"Ken","family":"Ueno","sequence":"additional","affiliation":[{"name":"Corporate R&amp;D Center, Toshiba Corporation, Kawasaki, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4135-8741","authenticated-orcid":false,"given":"Ryusei","family":"Shingaki","sequence":"additional","affiliation":[{"name":"Corporate R&amp;D Center, Toshiba Corporation, Kawasaki, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2770-0184","authenticated-orcid":false,"given":"Hisashi","family":"Kashima","sequence":"additional","affiliation":[{"name":"Department of Intelligence Science and Technology, Kyoto University, Kyoto, Japan"}]}],"member":"320","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Advances in Neural Information Processing Systems","volume":"27","author":"Ba Jimmy","year":"2014","unstructured":"Jimmy Ba and Rich Caruana. 2014. Do Deep Nets Really Need to be Deep?. In Advances in Neural Information Processing Systems, Vol. 27. Curran Associates, Inc., 1--9."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_2_1","DOI":"10.1021\/jf950305a"},{"unstructured":"Jonathan Crabb\u00e9 and Mihaela van der Schaar. 2021. Explaining Time Series Predictions with Dynamic Masks. In ICML. PMLR 2166--2177.","key":"e_1_3_2_1_3_1"},{"key":"e_1_3_2_1_4_1","volume-title":"Yanping, Bing Hu, Nurjahan Begum, Anthony Bagnall, Abdullah Mueen, Gustavo Batista, and Hexagon-ML.","author":"Dau Hoang Anh","year":"2018","unstructured":"Hoang Anh Dau, Eamonn Keogh, Kaveh Kamgar, Chin-Chia Yeh, Michael, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Yanping, Bing Hu, Nurjahan Begum, Anthony Bagnall, Abdullah Mueen, Gustavo Batista, and Hexagon-ML. 2018. The UCR Time Series Classification Archive. https:\/\/www.cs.ucr.edu\/ eamonn\/time_series_data_2018\/."},{"volume-title":"Class-Specific Explainability for Deep Time Series Classifiers","author":"Doddaiah Ramesh","unstructured":"Ramesh Doddaiah, Prathyush S. Parvatharaju, Elke A. Rundensteiner, and Thomas Hartvigsen. 2022. Class-Specific Explainability for Deep Time Series Classifiers. In ICDM. IEEE Computer Society, 101--110.","key":"e_1_3_2_1_5_1"},{"doi-asserted-by":"crossref","unstructured":"Souka\"ina Filali Boubrahimi and Shah Muhammad Hamdi. 2022. On the Mining of Time Series Data Counterfactual Explanations using Barycenters. In CIKM. ACM 3943--3947.","key":"e_1_3_2_1_6_1","DOI":"10.1145\/3511808.3557663"},{"volume-title":"Understanding Deep Networks via Extremal Perturbations and Smooth Masks","author":"Fong Ruth","unstructured":"Ruth Fong, Mandela Patrick, and Andrea Vedaldi. 2019. Understanding Deep Networks via Extremal Perturbations and Smooth Masks. In ICCV. IEEE Computer Society, 2950--2958.","key":"e_1_3_2_1_7_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_8_1","DOI":"10.1609\/aimag.v38i3.2741"},{"key":"e_1_3_2_1_9_1","first-page":"1","article-title":"Counterfactual explanations and how to find them: literature review and benchmarking","volume":"36","author":"Guidotti Riccardo","year":"2022","unstructured":"Riccardo Guidotti. 2022. Counterfactual explanations and how to find them: literature review and benchmarking. Data Min. Knowl. Discov., Vol. 36, 6 (2022), 1--55.","journal-title":"Data Min. Knowl. Discov."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_10_1","DOI":"10.1609\/aimag.v40i2.2850"},{"key":"e_1_3_2_1_11_1","volume-title":"Counterfactual explanation based on gradual construction for deep networks. Pattern Recogn","author":"Jung Hong-Gyu","year":"2022","unstructured":"Hong-Gyu Jung, Sin-Han Kang, Hee-Dong Kim, Dong-Ok Won, and Seong-Whan Lee. 2022. Counterfactual explanation based on gradual construction for deep networks. Pattern Recogn., Vol. 132, C (2022), 11 pages."},{"volume-title":"Explainable Time Series Tweaking via Irreversible and Reversible Temporal Transformations","author":"Karlsson Isak","unstructured":"Isak Karlsson, Jonathan Rebane, Panagiotis Papapetrou, and Aristides Gionis. 2018. Explainable Time Series Tweaking via Irreversible and Reversible Temporal Transformations. In ICDM. IEEE Computer Society, 207--216.","key":"e_1_3_2_1_12_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1007\/s10115-019-01389-4"},{"doi-asserted-by":"crossref","unstructured":"Jason Lines Luke M. Davis Jon Hills and Anthony Bagnall. 2012. A Shapelet Transform for Time Series Classification. In KDD. ACM 289--297.","key":"e_1_3_2_1_14_1","DOI":"10.1145\/2339530.2339579"},{"doi-asserted-by":"crossref","unstructured":"Chen Ling Junji Jiang Junxiang Wang and Zhao Liang. 2022. Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems. In KDD. ACM 1010--1020.","key":"e_1_3_2_1_15_1","DOI":"10.1145\/3534678.3539288"},{"doi-asserted-by":"crossref","unstructured":"Ramaravind K. Mothilal Amit Sharma and Chenhao Tan. 2020. Explaining machine learning classifiers through diverse counterfactual explanations. In FAT. ACM 607--617.","key":"e_1_3_2_1_16_1","DOI":"10.1145\/3351095.3372850"},{"key":"e_1_3_2_1_17_1","volume-title":"Rundensteiner","author":"Parvatharaju Prathyush S.","year":"2021","unstructured":"Prathyush S. Parvatharaju, Ramesh Doddaiah, Thomas Hartvigsen, and Elke A. Rundensteiner. 2021. Learning Saliency Maps to Explain Deep Time Series Classifiers. In CIKM. ACM, 1406--1415."},{"key":"e_1_3_2_1_18_1","volume-title":"Keogh","author":"Petitjean Francois","year":"2014","unstructured":"Francois Petitjean, Germain Forestier, Geoffrey I. Webb, Ann E. Nicholson, Yanping Chen, and Eamonn J. Keogh. 2014. Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification. In ICDM. IEEE Computer Society, 470--479."},{"unstructured":"Torty Sivill and Peter Flach. 2022. LIMESegment: Meaningful Realistic Time Series Explanations. In AISTATS. PMLR 3418--3433.","key":"e_1_3_2_1_19_1"},{"volume-title":"Interpretable Time-series Classification on Few-shot Samples","author":"Tang Wensi","unstructured":"Wensi Tang, Lu Liu, and Guodong Long. 2020. Interpretable Time-series Classification on Few-shot Samples. In IJCNN. IEEE Computer Society, 1--8.","key":"e_1_3_2_1_20_1"},{"key":"e_1_3_2_1_21_1","first-page":"841","article-title":"Counterfactual explanations without opening the black box: automated decisions and the GDPR","volume":"31","author":"Wachter S","year":"2018","unstructured":"S Wachter, B Mittelstadt, and C Russell. 2018. Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harvard Journal of Law and Technology, Vol. 31, 2 (2018), 841--887.","journal-title":"Harvard Journal of Law and Technology"},{"key":"e_1_3_2_1_22_1","volume-title":"Counterfactual-based Saliency Map: Towards Visual Contrastive Explanations for Neural Networks","author":"Wang Xue","year":"2042","unstructured":"Xue Wang, Zhibo Wang, Haiqin Weng, Hengchang Guo, Zhifei Zhang, Lu Jin, Tao Wei, and Kui Ren. 2023. Counterfactual-based Saliency Map: Towards Visual Contrastive Explanations for Neural Networks. In ICCV. IEEE Computer Society, 2042--2051."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_23_1","DOI":"10.1007\/s10994-023-06502-x"},{"volume-title":"Discovery Science","author":"Wang Zhendong","unstructured":"Zhendong Wang, Isak Samsten, Rami Mochaourab, and Panagiotis Papapetrou. 2021. Learning Time Series Counterfactuals via Latent Space Representations. In Discovery Science. Springer-Verlag, 369--384.","key":"e_1_3_2_1_24_1"},{"doi-asserted-by":"crossref","unstructured":"Akihiro Yamaguchi Ken Ueno and Hisashi Kashima. 2023. Time-Series Shapelets with Learnable Lengths. In CIKM. ACM 2866--2876.","key":"e_1_3_2_1_25_1","DOI":"10.1145\/3583780.3615082"},{"unstructured":"Lexiang Ye and Eamonn Keogh. 2009. Time Series Shapelets: A New Primitive for Data Mining. In KDD. ACM 947--956.","key":"e_1_3_2_1_26_1"}],"event":{"sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"],"acronym":"CIKM '24","name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","location":"Boise ID USA"},"container-title":["Proceedings of the 33rd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679952","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679952","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:09Z","timestamp":1750294689000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":26,"alternative-id":["10.1145\/3627673.3679952","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679952","relation":{},"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2024-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}