{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:07:30Z","timestamp":1767139650372,"version":"build-2238731810"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Singapore","isbn-type":[{"value":"9789811685309","type":"print"},{"value":"9789811685316","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-981-16-8531-6_2","type":"book-chapter","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T01:04:38Z","timestamp":1638925478000},"page":"16-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Taking the\u00a0Confusion Out of\u00a0Multinomial Confusion Matrices and\u00a0Imbalanced Classes"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3938-7586","authenticated-orcid":false,"given":"David","family":"Lovell","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0567-9115","authenticated-orcid":false,"given":"Bridget","family":"McCarron","sequence":"additional","affiliation":[]},{"given":"Brendan","family":"Langfield","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7376-5061","authenticated-orcid":false,"given":"Khoa","family":"Tran","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0109-6844","authenticated-orcid":false,"given":"Andrew P.","family":"Bradley","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,9]]},"reference":[{"issue":"12","key":"2_CR1","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.1109\/TVCG.2014.2346660","volume":"20","author":"B Alsallakh","year":"2014","unstructured":"Alsallakh, B., Hanbury, A., Hauser, H., Miksch, S., Rauber, A.: Visual methods for analyzing probabilistic classification data. IEEE Trans. Visual Comput. Graphics 20(12), 1703\u20131712 (2014). https:\/\/doi.org\/10.1109\/TVCG.2014.2346660","journal-title":"IEEE Trans. Visual Comput. Graphics"},{"key":"2_CR2","doi-asserted-by":"publisher","unstructured":"Caelen, O.: A Bayesian interpretation of the confusion matrix. Ann. Math. Artif. Intell. 81(3), 429\u2013450 (2017). https:\/\/doi.org\/10.1007\/s10472-017-9564-8","DOI":"10.1007\/s10472-017-9564-8"},{"issue":"9","key":"2_CR3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0222916","volume":"14","author":"R Delgado","year":"2019","unstructured":"Delgado, R., Tibau, X.A.: Why Cohen\u2019s Kappa should be avoided as performance measure in classification. PLoS ONE 14(9), e0222916 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0222916","journal-title":"PLoS ONE"},{"issue":"1","key":"2_CR4","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1016\/j.eswa.2006.10.016","volume":"34","author":"B Diri","year":"2008","unstructured":"Diri, B., Albayrak, S.: Visualization and analysis of classifiers performance in multi-class medical data. Expert Syst. Appl. 34(1), 628\u2013634 (2008). https:\/\/doi.org\/10.1016\/j.eswa.2006.10.016","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"2_CR5","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/BF01717448","volume":"10","author":"B Dujardin","year":"1994","unstructured":"Dujardin, B., Van den Ende, J., Van Gompel, A., Unger, J.P., Van der Stuyft, P.: Likelihood ratios: a real improvement for clinical decision making? Eur. J. Epidemiol. 10(1), 29\u201336 (1994). https:\/\/doi.org\/10.1007\/BF01717448","journal-title":"Eur. J. Epidemiol."},{"key":"2_CR6","doi-asserted-by":"publisher","unstructured":"Eddy, D.M.: Probabilistic reasoning in clinical medicine: Problems and opportunities. In: Tversky, A., Kahneman, D., Slovic, P. (eds.) Judgment under Uncertainty: Heuristics and Biases, pp. 249\u2013267. Cambridge University Press, Cambridge (1982). https:\/\/doi.org\/10.1017\/CBO9780511809477.019","DOI":"10.1017\/CBO9780511809477.019"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Etz, A., Wagenmakers, E.J.: J. B. S. Haldane\u2019s contribution to the bayes factor hypothesis test. Stat. Sci. 32(2), 313\u2013329 (2017)","DOI":"10.1214\/16-STS599"},{"issue":"5","key":"2_CR8","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1056\/NEJM197507312930513","volume":"293","author":"T Fagan","year":"1975","unstructured":"Fagan, T.: Nomogram for bayes\u2019s theorem. N. Engl. J. Med. 293(5), 257\u2013257 (1975). https:\/\/doi.org\/10.1056\/NEJM197507312930513","journal-title":"N. Engl. J. Med."},{"issue":"8","key":"2_CR9","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861\u2013874 (2006). https:\/\/doi.org\/10.1016\/j.patrec.2005.10.010","journal-title":"Pattern Recogn. Lett."},{"issue":"1","key":"2_CR10","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.patrec.2008.08.010","volume":"30","author":"C Ferri","year":"2009","unstructured":"Ferri, C., Hern\u00e1ndez-Orallo, J., Modroiu, R.: An experimental comparison of performance measures for classification. Pattern Recogn. Lett. 30(1), 27\u201338 (2009). https:\/\/doi.org\/10.1016\/j.patrec.2008.08.010","journal-title":"Pattern Recogn. Lett."},{"issue":"11","key":"2_CR11","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1016\/S0895-4356(03)00177-X","volume":"56","author":"AS Glas","year":"2003","unstructured":"Glas, A.S., Lijmer, J.G., Prins, M.H., Bonsel, G.J., Bossuyt, P.M.M.: The diagnostic odds ratio: a single indicator of test performance. J. Clin. Epidemiol. 56(11), 1129\u20131135 (2003). https:\/\/doi.org\/10.1016\/S0895-4356(03)00177-X","journal-title":"J. Clin. Epidemiol."},{"issue":"5","key":"2_CR12","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/j.compbiolchem.2004.09.006","volume":"28","author":"J Gorodkin","year":"2004","unstructured":"Gorodkin, J.: Comparing two K-category assignments by a K-category correlation coefficient. Comput. Biol. Chem. 28(5), 367\u2013374 (2004). https:\/\/doi.org\/10.1016\/j.compbiolchem.2004.09.006","journal-title":"Comput. Biol. Chem."},{"issue":"9469","key":"2_CR13","doi-asserted-by":"publisher","first-page":"1500","DOI":"10.1016\/S0140-6736(05)66422-7","volume":"365","author":"DA Grimes","year":"2005","unstructured":"Grimes, D.A., Schulz, K.F.: Refining clinical diagnosis with likelihood ratios. The Lancet 365(9469), 1500\u20131505 (2005). https:\/\/doi.org\/10.1016\/S0140-6736(05)66422-7","journal-title":"The Lancet"},{"key":"2_CR14","doi-asserted-by":"publisher","unstructured":"Hinterreiter, A., et al.: ConfusionFlow: a model-agnostic visualization for temporal analysis of classifier confusion. IEEE Trans. Visualization Comput. Graph., 1 (2020). https:\/\/doi.org\/10.1109\/TVCG.2020.3012063","DOI":"10.1109\/TVCG.2020.3012063"},{"key":"2_CR15","doi-asserted-by":"publisher","unstructured":"Jurman, G., Riccadonna, S., Furlanello, C.: A comparison of MCC and CEN error measures in multi-class prediction. PLoS ONE 7(8) (2012). https:\/\/doi.org\/10.1371\/journal.pone.0041882","DOI":"10.1371\/journal.pone.0041882"},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"Kuhn, M.: Building predictive models in r using the caret package. J. Stat. Softw. Articles 28(5), 1\u201326 (2008). https:\/\/doi.org\/10.18637\/jss.v028.i05","DOI":"10.18637\/jss.v028.i05"},{"issue":"7861","key":"2_CR17","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1038\/s41586-021-03512-4","volume":"594","author":"MY Lu","year":"2021","unstructured":"Lu, M.Y., et al.: AI-based pathology predicts origins for cancers of unknown primary. Nature 594(7861), 106\u2013110 (2021). https:\/\/doi.org\/10.1038\/s41586-021-03512-4","journal-title":"Nature"},{"key":"2_CR18","doi-asserted-by":"publisher","unstructured":"Luque, A., Carrasco, A., Mart\u00edn, A., de las Heras, A.: The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recogn. 91, 216\u2013231 (2019). https:\/\/doi.org\/10.1016\/j.patcog.2019.02.023","DOI":"10.1016\/j.patcog.2019.02.023"},{"issue":"1","key":"2_CR19","doi-asserted-by":"publisher","first-page":"5217","DOI":"10.1038\/s41467-018-07619-7","volume":"9","author":"L Maier-Hein","year":"2018","unstructured":"Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., et al.: Why rankings of biomedical image analysis competitions should be interpreted with care. Nat. Commun. 9(1), 5217 (2018). https:\/\/doi.org\/10.1038\/s41467-018-07619-7","journal-title":"Nat. Commun."},{"key":"2_CR20","doi-asserted-by":"publisher","unstructured":"Mullick, S.S., Datta, S., Dhekane, S.G., Das, S.: Appropriateness of performance indices for imbalanced data classification: an analysis. Pattern Recogn. 102 (2020). https:\/\/doi.org\/10.1016\/j.patcog.2020.107197","DOI":"10.1016\/j.patcog.2020.107197"},{"key":"2_CR21","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"2_CR22","unstructured":"R Core Team: R: A language and environment for statistical computing. Technical report, Vienna, Austria (2020). https:\/\/www.R-project.org\/, R Foundation for Statistical Computing"},{"key":"2_CR23","doi-asserted-by":"publisher","unstructured":"Raji, I.D., Smart, A., White, R.N., Mitchell, M., Gebru, T., Hutchinson, B., et al.: Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 33\u201344. ACM, Barcelona (2020). https:\/\/doi.org\/10.1145\/3351095.3372873","DOI":"10.1145\/3351095.3372873"},{"issue":"1","key":"2_CR24","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TVCG.2016.2598828","volume":"23","author":"D Ren","year":"2017","unstructured":"Ren, D., Amershi, S., Lee, B., Suh, J., Williams, J.D.: Squares: supporting interactive performance analysis for multiclass classifiers. IEEE Trans. Visual Comput. Graphics 23(1), 61\u201370 (2017). https:\/\/doi.org\/10.1109\/TVCG.2016.2598828","journal-title":"IEEE Trans. Visual Comput. Graphics"},{"key":"2_CR25","unstructured":"Sanderson, G.: The medical test paradox: can redesigning Bayes rule help? (2020). https:\/\/www.youtube.com\/watch?v=lG4VkPoG3ko"},{"issue":"4","key":"2_CR26","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","volume":"45","author":"M Sokolova","year":"2009","unstructured":"Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manage. 45(4), 427\u2013437 (2009). https:\/\/doi.org\/10.1016\/j.ipm.2009.03.002","journal-title":"Inf. Process. Manage."},{"key":"2_CR27","unstructured":"Thoma, M.: The HASYv2 dataset. arXiv:1701.08380 [cs] (2017)"},{"key":"2_CR28","doi-asserted-by":"publisher","unstructured":"Verma, S., Rubin, J.: Fairness definitions explained. In: Proceedings of the International Workshop on Software Fairness, FairWare 2018, pp. 1\u20137. ACM, New York (2018). https:\/\/doi.org\/10.1145\/3194770.3194776","DOI":"10.1145\/3194770.3194776"},{"key":"2_CR29","volume-title":"Information visualization: perception for design. Interactive technologies","author":"C Ware","year":"2013","unstructured":"Ware, C.: Information visualization: perception for design. Interactive technologies, 3rd edn. Morgan Kaufmann, Waltham (2013)","edition":"3"},{"issue":"5","key":"2_CR30","doi-asserted-by":"publisher","first-page":"3799","DOI":"10.1016\/j.eswa.2009.11.040","volume":"37","author":"JM Wei","year":"2010","unstructured":"Wei, J.M., Yuan, X.J., Hu, Q.H., Wang, S.Q.: A novel measure for evaluating classifiers. Expert Syst. Appl. 37(5), 3799\u20133809 (2010). https:\/\/doi.org\/10.1016\/j.eswa.2009.11.040","journal-title":"Expert Syst. Appl."},{"key":"2_CR31","doi-asserted-by":"publisher","unstructured":"Wickham, H., et al.: Welcome to the tidyverse. J. Open Source Softw. 4(43), 1686 (2019). https:\/\/doi.org\/10.21105\/joss.01686","DOI":"10.21105\/joss.01686"},{"key":"2_CR32","unstructured":"Wu, X.Z., Zhou, Z.H.: A unified view of multi-label performance measures. arXiv:1609.00288 [cs] (2017)"},{"key":"2_CR33","doi-asserted-by":"publisher","unstructured":"Zadrozny, B., Elkan, C.: Transforming classifier scores into accurate multiclass probability estimates. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 694\u2013699. Association for Computing Machinery, New York (2002). https:\/\/doi.org\/10.1145\/775047.775151","DOI":"10.1145\/775047.775151"},{"key":"2_CR34","unstructured":"Zhou, Z.H., Liu, X.Y.: On multi-class cost-sensitive learning. In: Proceedings of the 21st National Conference on Artificial Intelligence, AAAI 2006, vol. 1, pp. 567\u2013572. AAAI Press, Boston (2006)"},{"key":"2_CR35","doi-asserted-by":"publisher","first-page":"40","DOI":"10.3389\/fhumd.2021.688152","volume":"3","author":"RV Zicari","year":"2021","unstructured":"Zicari, R.V., Ahmed, S., Amann, J., Braun, S.A., Brodersen, J., et al.: Co-design of a trustworthy AI system in healthcare: deep learning based skin lesion classifier. Front. Hum. Dyn. 3, 40 (2021). https:\/\/doi.org\/10.3389\/fhumd.2021.688152","journal-title":"Front. Hum. Dyn."}],"updated-by":[{"DOI":"10.1007\/978-981-16-8531-6_17","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000}}],"container-title":["Communications in Computer and Information Science","Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-8531-6_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T11:08:24Z","timestamp":1648724904000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-8531-6_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811685309","9789811685316"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-8531-6_2","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"9 December 2021","order":2,"name":"change_date","label":"Change Date","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Correction","order":3,"name":"change_type","label":"Change Type","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"In the originally published version of chapter 2, the Table 1. contained an error in a formula. The formula error in Table 1. has been corrected.","order":4,"name":"change_details","label":"Change Details","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AusDM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Conference on Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brisbane, QLD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ausdm2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ausdm21.ausdm.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}