{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:27:02Z","timestamp":1740101222795,"version":"3.37.3"},"reference-count":81,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T00:00:00Z","timestamp":1666569600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T00:00:00Z","timestamp":1666569600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100004343","name":"3M","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004343","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,24]]},"DOI":"10.1109\/sibgrapi55357.2022.9991759","type":"proceedings-article","created":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T19:42:48Z","timestamp":1672083768000},"page":"294-301","source":"Crossref","is-referenced-by-count":1,"title":["Visualization for Machine Learning"],"prefix":"10.1109","author":[{"given":"Peter","family":"Xenopoulos","sequence":"first","affiliation":[{"name":"New York University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luis Gustavo","family":"Nonato","sequence":"additional","affiliation":[{"name":"Universidade de S&#x02DC;ao Paulo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudio","family":"Silva","sequence":"additional","affiliation":[{"name":"New York University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/vast47406.2019.8986948"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/tvcg.2017.2744878"},{"key":"ref3","article-title":"Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation","author":"Powers","year":"2020","journal-title":"arXiv:2010.16061"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2014.2346660"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2016.2598828"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/1518701.1518895"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3491102.3501823"},{"key":"ref8","first-page":"1321","article-title":"On calibration of modern neural networks","volume":"70","author":"Guo","year":"2017","journal-title":"ICML"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.2307\/2987588"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-010-1276-8_19"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.2307\/2346866"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0450(1963)002<0191:OSPF>2.0.CO;2"},{"key":"ref13","first-page":"38","article-title":"Measuring calibration in deep learning","author":"Nixon","year":"2019","journal-title":"IEEE CVPR"},{"key":"ref14","first-page":"3459","article-title":"Evaluating model calibration in classification","volume-title":"Int. Conf. Artif. Intel. Stat.","volume":"89","author":"Vaicenavicius"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/tvcg.2022.3209489"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376177"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2013.125"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.32614\/RJ-2017-046"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2865043"},{"key":"ref20","article-title":"Interpretml: A unified framework for machine learning interpretability","author":"Nori","year":"2019","journal-title":"arXiv:1909.09223"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1080\/03610926.2011.628772"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/visual.2019.8933695"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300809"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2019.1629942"},{"key":"ref26","article-title":"GAM changer: Editing generalized additive models with interactive visualization","volume":"abs\/2112.03245","author":"Wang","year":"2021","journal-title":"CoRR"},{"issue":"1","key":"ref27","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine learning"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref29","first-page":"3146","article-title":"Lightgbm: A highly efficient gradient boosting decision tree","author":"Ke","year":"2017","journal-title":"NIPS"},{"key":"ref30","first-page":"6639","article-title":"Catboost: unbiased boosting with categorical features","author":"Prokhorenkova","year":"2018","journal-title":"NIPS"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/VAST.2011.6102453"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2864475"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2020.3030354"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-10925-7_40"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3236009"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"ref37","first-page":"4765","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017","journal-title":"NIPS"},{"key":"ref38","first-page":"3319","article-title":"Axiomatic attribution for deep networks","volume-title":"Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, ser. Proceedings of Machine Learning Research","volume":"70","author":"Sundararajan"},{"key":"ref39","article-title":"Captum: A unified and generic model interpretability library for pytorch","author":"Kokhlikyan","year":"2020","journal-title":"arXiv preprint arXiv:2009.07896"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3077257.3077260"},{"key":"ref41","article-title":"Subplex: Towards a better understanding of black box model explanations at the subpopulation level","author":"Chan","year":"2020","journal-title":"arXiv:2007.10609"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2022.3152450"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2017.74"},{"key":"ref44","article-title":"Melody: generating and visualizing machine learning model summary to understand data and classifiers together","author":"Chan","year":"2020","journal-title":"arXiv:2007.10614"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2846735"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1002\/wics.101"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-33037-0_14"},{"issue":"11","key":"ref48","article-title":"Visualizing data using t-sne","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref49","article-title":"Umap: Uniform manifold approximation and projection for dimension reduction","author":"McInnes","year":"2018","journal-title":"arXiv:1802.03426"},{"issue":"1998","key":"ref50","first-page":"1","article-title":"Linear discriminant analysis-a brief tutorial","volume":"18","author":"Balakrishnama","year":"1998","journal-title":"Inst. for Signal and information Processing"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.09.064"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2016.2598838"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14534"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2007.70443"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2011.220"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2012.03107.x"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/VAST.2012.6400489"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/s00454-004-1146-y"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1090\/conm\/453\/08802"},{"key":"ref60","first-page":"39:1","article-title":"giotto-tda:: A topological data analysis toolkit for machine learning and data exploration","volume":"22","author":"Tauzin","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.21105\/joss.01315"},{"key":"ref62","first-page":"91","article-title":"Topological methods for the analysis of high dimensional data sets and 3d object recognition","volume-title":"4th Symposium on Point Based Graphics, PBG@Eurographics 2007","author":"Singh"},{"key":"ref63","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-71507-0","volume-title":"Topological Data Analysis for Scientific Visualization, ser. Mathematics and visualization","author":"Tierny","year":"2017"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/tvcg.2019.2934594"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2017.12.982"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms8723"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/tvcg.2017.2743980"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376846"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/tvcg.2014.2346449"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/tvcg.2020.3030441"},{"key":"ref71","article-title":"Topological representations of local explanations","volume":"abs\/2201.02155","author":"Xenopoulos","year":"2022","journal-title":"CoRR"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/tvcg.2018.2843369"},{"article-title":"Striving for simplicity: The all convolutional net","volume-title":"3rd International Conference on Learning Representations, ICLR 2015","author":"Springenberg","key":"ref73"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2016.2598831"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2020.3030418"},{"key":"ref76","first-page":"2673","article-title":"Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)","volume-title":"Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, July 10-15, 2018, ser. Proceedings of Machine Learning Research","volume":"80","author":"Kim"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/tvcg.2019.2934659"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.3934\/mfc.2018008"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744158"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/VAST.2017.8585721"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744718"}],"event":{"name":"2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","start":{"date-parts":[[2022,10,24]]},"location":"Natal, Brazil","end":{"date-parts":[[2022,10,27]]}},"container-title":["2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9991741\/9991742\/09991759.pdf?arnumber=9991759","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T09:19:42Z","timestamp":1709371182000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9991759\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,24]]},"references-count":81,"URL":"https:\/\/doi.org\/10.1109\/sibgrapi55357.2022.9991759","relation":{},"subject":[],"published":{"date-parts":[[2022,10,24]]}}}