{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:58:09Z","timestamp":1742957889685,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031453885"},{"type":"electronic","value":"9783031453892"}],"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\/978-3-031-45389-2_24","type":"book-chapter","created":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T15:03:39Z","timestamp":1697036619000},"page":"353-367","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Framework for\u00a0Characterizing What Makes an\u00a0Instance Hard to\u00a0Classify"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3631-156X","authenticated-orcid":false,"given":"Maria Gabriela","family":"Valeriano","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9768-7563","authenticated-orcid":false,"given":"Pedro Yuri Arbs","family":"Paiva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1122-0693","authenticated-orcid":false,"given":"Carlos Roberto Veiga","family":"Kiffer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6140-571X","authenticated-orcid":false,"given":"Ana Carolina","family":"Lorena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"key":"24_CR1","doi-asserted-by":"publisher","unstructured":"Anderson, D., Bjarnadottir, M.V., Nenova, Z.: Machine learning in healthcare: operational and financial impact. In: Babich, V., Birge, J.R., Hilary, G. (eds.) Innovative Technology at the Interface of Finance and Operations, vol. 11, pp. 153\u2013174. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-75729-8_5","DOI":"10.1007\/978-3-030-75729-8_5"},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Imrie, F., Cebere, B., McKinney, E.F., van der Schaar M.: AutoPrognosis 2.0: democratizing diagnostic and prognostic modeling in healthcare with automated machine learning. arXiv preprint arXiv:2210.12090 (2022)","DOI":"10.1371\/journal.pdig.0000276"},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"de Moraes, B.A.F., Miraglia, J., Donato, T., Filho, A.: Covid-19 diagnosis prediction in emergency care patients: a machine learning approach. MedRxiv, 2020-04 (2020)","DOI":"10.1101\/2020.04.04.20052092"},{"issue":"1","key":"24_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-82885-y","volume":"11","author":"FT Fernandes","year":"2021","unstructured":"Fernandes, F.T., de Oliveira, T.A., Teixeira, C.E., de Moraes Batista, A.F., Dalla Costa, G., Chiavegatto Filho, A.D.P.: A multipurpose machine learning approach to predict covid-19 negative prognosis in S\u00e3o Paulo, Brazil. Sci. Rep. 11(1), 1\u20137 (2021)","journal-title":"Sci. Rep."},{"key":"24_CR5","doi-asserted-by":"publisher","unstructured":"Wynants, L., et al.: Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 369 (2020). https:\/\/doi.org\/10.1136\/bmj.m1328","DOI":"10.1136\/bmj.m1328"},{"key":"24_CR6","unstructured":"Seedat, N., Crabbe J., van der Schaar, M.: Data-SUITE: data-centric identification of in-distribution incongruous examples. arXiv preprint arXiv:2202.08836 (2022)"},{"key":"24_CR7","unstructured":"Seedat, N., Crabbe J., Bica, I., van der Schaar, M.: Data-IQ: characterizing subgroups with heterogeneous outcomes in tabular data. arXiv preprint arXiv:2210.13043 (2022)"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Paiva, P.Y.A., Moreno, C.C., Smith-Miles, K., Valeriano, M.G., Lorena, A.C.: Relating instance hardness to classification performance in a dataset: a visual approach. Mach. Learn., 1\u201339 (2022)","DOI":"10.1007\/s10994-022-06205-9"},{"issue":"2","key":"24_CR9","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s10994-013-5422-z","volume":"95","author":"MR Smith","year":"2014","unstructured":"Smith, M.R., Martinez, T., Giraud-Carrier, C.: An instance level analysis of data complexity. Mach. Learn. 95(2), 225\u2013256 (2014)","journal-title":"Mach. Learn."},{"key":"24_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-3-030-61380-8_33","volume-title":"Intelligent Systems","author":"JLM Arruda","year":"2020","unstructured":"Arruda, J.L.M., Prud\u00eancio, R.B.C., Lorena, A.C.: Measuring instance hardness using data complexity measures. In: Cerri, R., Prati, R.C. (eds.) BRACIS 2020. LNCS (LNAI), vol. 12320, pp. 483\u2013497. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61380-8_33"},{"key":"24_CR11","unstructured":"Paiva, P.Y.A., Smith-Miles, K., Valeriano, M.G., Lorena, A.C.: PyHard: a novel tool for generating hardness embeddings to support data-centric analysis. arXiv preprint arXiv:2109.14430 (2021)"},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Valeriano, M.G., et al.: Let the data speak: analysing data from multiple health centers of the S\u00e3o Paulo metropolitan area for covid-19 clinical deterioration prediction. In: 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 948\u2013951. IEEE (2022)","DOI":"10.1109\/CCGrid54584.2022.00115"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Zheng, K., Chen, G., Herschel, M., Ngiam, K.Y., Ooi, B.C., Gao, J.: PACE: learning effective task decomposition for human-in-the-loop healthcare delivery. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2156\u20132168 (2021)","DOI":"10.1145\/3448016.3457281"},{"issue":"1","key":"24_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-03825-4","volume":"11","author":"A Houston","year":"2021","unstructured":"Houston, A., Cosma, G., Turner, P., Bennett, A.: Predicting surgical outcomes for chronic exertional compartment syndrome using a machine learning framework with embedded trust by interrogation strategies. Sci. Rep. 11(1), 1\u201315 (2021)","journal-title":"Sci. Rep."},{"key":"24_CR15","doi-asserted-by":"publisher","unstructured":"Prud\u00eancio, R.B., Silva Filho, T.M.: Explaining learning performance with local performance regions and maximally relevant meta-rules. In: Xavier-Junior, J.C., Rios, R.A. (eds.) Brazilian Conference on Intelligent Systems, pp. 550\u2013564. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-21686-2_38","DOI":"10.1007\/978-3-031-21686-2_38"},{"key":"24_CR16","doi-asserted-by":"crossref","unstructured":"Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.-Z.: XAI-explainable artificial intelligence. Sci. Rob. 4(37), eaay7120 (2019)","DOI":"10.1126\/scirobotics.aay7120"},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Ojala, M., Garriga, G.C.: Permutation tests for studying classifier performance. J. Mach. Learn. Res. 11(6) (2010)","DOI":"10.1109\/ICDM.2009.108"},{"key":"24_CR18","unstructured":"Ghorbani, A., Zou, J.: Data Shapley: equitable valuation of data for machine learning. In: International Conference on Machine Learning, pp. 2242\u20132251. PMLR (2019)"},{"issue":"5","key":"24_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3347711","volume":"52","author":"AC Lorena","year":"2019","unstructured":"Lorena, A.C., Garcia, L.P., Lehmann, J., Souto, M.C., Ho, T.K.: How complex is your classification problem? A survey on measuring classification complexity. ACM Comput. Surv. 52(5), 1\u201334 (2019)","journal-title":"ACM Comput. Surv."},{"issue":"2","key":"24_CR20","doi-asserted-by":"publisher","DOI":"10.1111\/sji.12967","volume":"93","author":"A Jafarzadeh","year":"2021","unstructured":"Jafarzadeh, A., Jafarzadeh, S., Nozari, P., Mokhtari, P., Nemati, M.: Lymphopenia an important immunological abnormality in patients with covid-19: possible mechanisms. Scand. J. Immunol. 93(2), e12967 (2021)","journal-title":"Scand. J. Immunol."},{"key":"24_CR21","unstructured":"Amankwaa-Kyeremeh, B., Greet, C., Zanin, M., Skinner, W., Asamoah, R.K.: Selecting key predictor parameters for regression analysis using modified Neighbourhood Component Analysis (NCA) algorithm. In: Proceedings of 6th UMaT Biennial International Mining and Mineral Conference, pp. 320\u2013325 (2020)"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Smith-Miles, K., Tan, T.T.: Measuring algorithm footprints in instance space. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1\u20138. IEEE (2012)","DOI":"10.1109\/CEC.2012.6252992"},{"issue":"1","key":"24_CR23","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/s10994-017-5629-5","volume":"107","author":"MA Mu\u00f1oz","year":"2018","unstructured":"Mu\u00f1oz, M.A., Villanova, L., Baatar, D., Smith-Miles, K.: Instance spaces for machine learning classification. Mach. Learn. 107(1), 109\u2013147 (2018)","journal-title":"Mach. Learn."},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Khan, K., Rehman, S.U., Aziz, K., Fong, S., Sarasvady, S.: DBSCAN: past, present and future. In: The Fifth International Conference on the Applications of Digital Information and Web Technologies, pp. 232\u2013238. IEEE (2014)","DOI":"10.1109\/ICADIWT.2014.6814687"},{"key":"24_CR25","first-page":"1","volume":"27","author":"H Edelsbrunner","year":"2010","unstructured":"Edelsbrunner, H.: Alpha shapes-a survey. Tessellations Sci. 27, 1\u201325 (2010)","journal-title":"Tessellations Sci."},{"issue":"1","key":"24_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-014-0007-7","volume":"2","author":"MM Najafabadi","year":"2015","unstructured":"Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R., Muharemagic, E.: Deep learning applications and challenges in big data analytics. J. Big Data 2(1), 1\u201321 (2015)","journal-title":"J. Big Data"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45389-2_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:11:27Z","timestamp":1710346287000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45389-2_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031453885","9783031453892"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45389-2_24","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":"12 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belo Horizonte","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","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":"25 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":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.bracis.dcc.ufmg.br","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"JEMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"242","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":"90","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":"0","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":"37% - 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":"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":"5","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)"}}]}}