{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T18:15:48Z","timestamp":1780337748700,"version":"3.54.1"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universit\u00e0 degli Studi di Roma La Sapienza"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Machine Learning is currently a well-suited approach widely adopted for solving data-driven problems in predictive maintenance. Data-driven approaches can be used as the main building block in risk-based assessment and analysis tools for Transmission and Distribution System Operators in modern Smart Grids. For this purpose, a suitable Decision Support System should be able of providing not only early warnings, such as the detection of faults in real time, but even an accurate probability estimate of outages and failures. In other words, the performance of classification systems, at least in these cases, needs to be assessed even in terms of reliable outputting posterior probabilities, a really important feature that, in general, classifiers very often do not offer. In this paper are compared several state-of-the-art calibration techniques along with a set of simple new proposed techniques, with the aim of calibrating fuzzy scoring values of a custom-made evolutionary-cluster-based hybrid classifier trained on a set of a real-world dataset of faults collected within the power grid that feeds the city of Rome, Italy. Comparison results show that in real-world cases calibration techniques need to be assessed carefully depending on the scores distribution and the proposed techniques are a valid alternative to the ones existing in the technical literature in terms of calibration performance, computational efficiency and flexibility.<\/jats:p>","DOI":"10.1007\/s00500-022-07194-6","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T10:04:55Z","timestamp":1655978695000},"page":"7175-7193","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Estimation of fault probability in medium voltage feeders through calibration techniques in classification models"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4915-0723","authenticated-orcid":false,"given":"Enrico","family":"De Santis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Arn\u00f2","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonello","family":"Rizzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,23]]},"reference":[{"key":"7194_CR1","unstructured":"ACEA (2016) The ACEA smart grid pilot project (in Italian). https:\/\/ses.jrc.ec.europa.eu\/acea-distribuzione-smart-grid-pilot-project"},{"key":"7194_CR2","doi-asserted-by":"crossref","unstructured":"Afzal M, Pothamsetty V (2012) Analytics for distributed smart grid sensing. In: 2012 IEEE PES innovative smart grid technologies (ISGT), pp 1\u20137","DOI":"10.1109\/ISGT.2012.6175733"},{"key":"7194_CR3","unstructured":"Asuncion A, Newman D (2007) UCI machine learning repository"},{"key":"7194_CR4","doi-asserted-by":"crossref","unstructured":"Ayer M, Brunk HD, Ewing GM, Reid WT, Silverman E (1955) An empirical distribution function for sampling with incomplete information. Ann Math Stat 26(4):641\u2013647. http:\/\/www.jstor.org\/stable\/2236377","DOI":"10.1214\/aoms\/1177728423"},{"key":"7194_CR5","doi-asserted-by":"crossref","unstructured":"Bhattacharya B, Sinha A (2017) Intelligent fault analysis in electrical power grids. In: 2017 IEEE 29th international conference on tools with artificial intelligence (ICTAI). https:\/\/doi.org\/10.1109\/ictai.2017.00151","DOI":"10.1109\/ICTAI.2017.00151"},{"key":"7194_CR6","doi-asserted-by":"crossref","unstructured":"Blair CG, Thompson J, Robertson NM (2014) Introspective classification for pedestrian detection. In: 2014 sensor signal processing for defence (SSPD), pp 1\u20135","DOI":"10.1109\/SSPD.2014.6943310"},{"issue":"1","key":"7194_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1175\/1520-0493","volume":"78","author":"GW Brier","year":"1950","unstructured":"Brier GW (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 78(1):1. https:\/\/doi.org\/10.1175\/1520-0493","journal-title":"Mon Weather Rev"},{"key":"7194_CR8","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: Synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357. https:\/\/doi.org\/10.1613\/jair.953","journal-title":"J Artif Intell Res"},{"key":"7194_CR9","unstructured":"Cremer JL, Strbac G (2019) A machine-learning based probabilistic perspective on dynamic security assessment. arXiv:1912.07477"},{"issue":"1","key":"7194_CR10","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1109\/TPWRD.2005.852273","volume":"21","author":"B Das","year":"2006","unstructured":"Das B (2006) Fuzzy logic-based fault-type identification in unbalanced radial power distribution system. IEEE Trans Power Deliv 21(1):278\u2013285","journal-title":"IEEE Trans Power Deliv"},{"key":"7194_CR11","doi-asserted-by":"publisher","unstructured":"De\u00a0Santis E, Livi L, Mascioli F, Sadeghian A, Rizzi A (2014) Fault recognition in smart grids by a one-class classification approach. In: Neural networks (IJCNN), 2014 international joint conference on, pp 1949\u20131956. https:\/\/doi.org\/10.1109\/IJCNN.2014.6889668","DOI":"10.1109\/IJCNN.2014.6889668"},{"key":"7194_CR12","doi-asserted-by":"publisher","unstructured":"De\u00a0Santis E, Rizzi A, Sadeghian A, Frattale\u00a0Mascioli F (2015a) A learning intelligent system for fault detection in smart grid by a one-class classification approach. In: Neural networks (IJCNN), 2015 international joint conference on, pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN.2015.7280756","DOI":"10.1109\/IJCNN.2015.7280756"},{"key":"7194_CR13","doi-asserted-by":"publisher","unstructured":"De\u00a0Santis E, Rizzi A, Sadeghian A, Mascioli F (2015b) A learning intelligent system for fault detection in smart grid by a one-class classification approach. In: 2015 international joint conference on neural networks (IJCNN), pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN.2015.7280756","DOI":"10.1109\/IJCNN.2015.7280756"},{"key":"7194_CR14","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/j.neucom.2015.05.112","volume":"170","author":"ED De Santis","year":"2015","unstructured":"De Santis ED, Livi L, Sadeghian A, Rizzi A (2015c) Modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one-class classification. Neurocomputing 170:368\u2013383. https:\/\/doi.org\/10.1016\/j.neucom.2015.05.112","journal-title":"Neurocomputing"},{"key":"7194_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2017.10.007","author":"E De Santis","year":"2017","unstructured":"De Santis E, Rizzi A, Sadeghian A (2017) A cluster-based dissimilarity learning approach for localized fault classification in smart grids. Swarm Evolut Comput. https:\/\/doi.org\/10.1016\/j.swevo.2017.10.007","journal-title":"Swarm Evolut Comput"},{"key":"7194_CR16","doi-asserted-by":"crossref","unstructured":"De\u00a0Santis E, Martino A, Rizzi A, Mascioli FMF (2018a) Dissimilarity space representations and automatic feature selection for protein function prediction. In: 2018 international joint conference on neural networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN.2018.8489115"},{"key":"7194_CR17","doi-asserted-by":"publisher","unstructured":"De Santis E, Paschero M, Rizzi A, Mascioli FMF (2018b) Evolutionary optimization of an affine model for vulnerability characterization in smart grids. In: 2018 international joint conference on neural networks (IJCNN), pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN.2018.8489749","DOI":"10.1109\/IJCNN.2018.8489749"},{"key":"7194_CR18","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.swevo.2017.10.007","volume":"39","author":"E De Santis","year":"2018","unstructured":"De Santis E, Rizzi A, Sadeghian A (2018c) A cluster-based dissimilarity learning approach for localized fault classification in smart grids. Swarm Evol Comput 39:267\u2013278","journal-title":"Swarm Evol Comput"},{"key":"7194_CR19","doi-asserted-by":"crossref","unstructured":"DeGroot MH, Fienberg SE (1983a) The comparison and evaluation of forecasters. J R Stat Soc Ser D (Stat) 32(1\/2):12\u201322. http:\/\/www.jstor.org\/stable\/2987588","DOI":"10.2307\/2987588"},{"key":"7194_CR20","doi-asserted-by":"crossref","unstructured":"DeGroot MH, Fienberg SE (1983b) The comparison and evaluation of forecasters. J R Stat Soc Ser D (Stat) 32(1\/2):12\u201322. http:\/\/www.jstor.org\/stable\/2987588","DOI":"10.2307\/2987588"},{"key":"7194_CR21","unstructured":"Dua D, Graff C (2019) UCI machine learning repository. University of California, School of Information and Computer Science. Irvine, CA. http:\/\/archive.ics.uci.edu\/ml"},{"key":"7194_CR22","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/BF01025868","volume":"57","author":"DA Freedman","year":"1981","unstructured":"Freedman DA, Diaconis P (1981) On the histogram as a density estimator: L2 theory. Z Wahrscheinlichkeitstheor Verwa Geb 57:453\u2013476","journal-title":"Z Wahrscheinlichkeitstheor Verwa Geb"},{"key":"7194_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-018-3577-z","author":"G Gosztolya","year":"2018","unstructured":"Gosztolya G, Busa-Fekete R (2018) Calibrating adaboost for phoneme classification. Soft Comput. https:\/\/doi.org\/10.1007\/s00500-018-3577-z","journal-title":"Soft Comput"},{"issue":"3","key":"7194_CR24","doi-asserted-by":"publisher","first-page":"1549","DOI":"10.1109\/TPWRD.2005.860238","volume":"21","author":"SD Guikema","year":"2006","unstructured":"Guikema SD, Davidson RA, Liu H (2006) Statistical models of the effects of tree trimming on power system outages. IEEE Trans Power Deliv 21(3):1549\u20131557","journal-title":"IEEE Trans Power Deliv"},{"key":"7194_CR25","unstructured":"Gunning D (2017) Explainable artificial intelligence (XAI). Defense Adv Res Proj Agency (DARPA), nd Web 2:2"},{"key":"7194_CR26","unstructured":"Hajek P, Godo L, Esteva F (2013) Fuzzy logic and probability. In: Proc of UAI\u201995"},{"key":"7194_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5","volume-title":"The elements of statistical learning","author":"T Hastie","year":"2001","unstructured":"Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer Series in Statistics, Springer, New York"},{"issue":"6","key":"7194_CR28","doi-asserted-by":"publisher","first-page":"2947","DOI":"10.1109\/TSG.2014.2330624","volume":"5","author":"H Jiang","year":"2014","unstructured":"Jiang H, Zhang JJ, Gao W, Wu Z (2014) Fault detection, identification, and location in smart grid based on data-driven computational methods. IEEE Trans Smart Grid 5(6):2947\u20132956","journal-title":"IEEE Trans Smart Grid"},{"key":"7194_CR29","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1007\/978-3-642-17080-5_21","volume-title":"Artificial intelligence and cognitive science","author":"SS Khan","year":"2010","unstructured":"Khan SS, Madden MG (2010) A survey of recent trends in one class classification. In: Coyle L, Freyne J (eds) Artificial intelligence and cognitive science. Springer, Heidelberg, pp 188\u2013197"},{"key":"7194_CR30","doi-asserted-by":"crossref","unstructured":"Kordestani M, Saif M (2017) Data fusion for fault diagnosis in smart grid power systems. In: 2017 IEEE 30th Canadian conference on electrical and computer engineering (CCECE), pp 1\u20136","DOI":"10.1109\/CCECE.2017.7946717"},{"issue":"3","key":"7194_CR31","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/s10994-007-5018-6","volume":"68","author":"HT Lin","year":"2007","unstructured":"Lin HT, Lin CJ, Weng RC (2007) A note on Platt\u2019s probabilistic outputs for support vector machines. Mach Learn 68(3):267\u2013276. https:\/\/doi.org\/10.1007\/s10994-007-5018-6","journal-title":"Mach Learn"},{"key":"7194_CR32","unstructured":"Lucena B (2018) Spline-based probability calibration. arXiv:1809.07751"},{"issue":"2","key":"7194_CR33","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1109\/TPWRD.2008.915809","volume":"23","author":"X Luo","year":"2008","unstructured":"Luo X, Kezunovic M (2008) Implementing fuzzy reasoning petri-nets for fault section estimation. IEEE Trans Power Deliv 23(2):676\u2013685","journal-title":"IEEE Trans Power Deliv"},{"key":"7194_CR34","doi-asserted-by":"publisher","unstructured":"Martino A, De\u00a0Santis E, Baldini L, Rizzi A (2019) Calibration techniques for binary classification problems: a comparative analysis. In: IJCCI, pp 487\u2013495. https:\/\/doi.org\/10.5220\/0008165504870495","DOI":"10.5220\/0008165504870495"},{"issue":"1","key":"7194_CR35","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/TPWRS.2019.2928276","volume":"35","author":"P Massaferro","year":"2020","unstructured":"Massaferro P, Martino JMD, Fern\u00e1ndez A (2020) Fraud detection in electric power distribution: An approach that maximizes the economic return. IEEE Trans Power Syst 35(1):703\u2013710","journal-title":"IEEE Trans Power Syst"},{"issue":"3","key":"7194_CR36","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1109\/5.364485","volume":"83","author":"JM Mendel","year":"1995","unstructured":"Mendel JM (1995) Fuzzy logic systems for engineering: a tutorial. Proc IEEE 83(3):345\u2013377. https:\/\/doi.org\/10.1109\/5.364485","journal-title":"Proc IEEE"},{"issue":"2","key":"7194_CR37","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1109\/TPWRS.2003.821036","volume":"19","author":"S-W Min","year":"2004","unstructured":"Min S-W, Sohn J-M, Park J-K, Kim K-H (2004) Adaptive fault section estimation using matrix representation with fuzzy relations. IEEE Trans Power Syst 19(2):842\u2013848","journal-title":"IEEE Trans Power Syst"},{"issue":"2","key":"7194_CR38","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1177\/0272989X9601600205","volume":"16","author":"AP Morise","year":"1996","unstructured":"Morise AP, Diamond GA, Detrano R, Bobbio M, Gunel E (1996) The effect of disease-prevalence adjustments on the accuracy of a logistic prediction model. Med Dec Mak 16(2):133\u2013142. https:\/\/doi.org\/10.1177\/0272989X9601600205 (PMID: 8778531)","journal-title":"Med Dec Mak"},{"key":"7194_CR39","doi-asserted-by":"crossref","unstructured":"Murphy AH, Winkler RL (1977) Reliability of subjective probability forecasts of precipitation and temperature. Journal of the Royal Statistical Society Series C (Applied Statistics) 26(1):41\u201347, http:\/\/www.jstor.org\/stable\/2346866","DOI":"10.2307\/2346866"},{"key":"7194_CR40","doi-asserted-by":"crossref","unstructured":"Naeini MP, Cooper GF, Hauskrecht M (2015) Obtaining well calibrated probabilities using Bayesian binning. In: Proceedings of the 29th AAAI conference on artificial intelligence. AAAI Press, AAAI\u201915, pp 2901\u20132907. http:\/\/dl.acm.org\/citation.cfm?id=2888116.2888120","DOI":"10.1609\/aaai.v29i1.9602"},{"key":"7194_CR41","doi-asserted-by":"publisher","unstructured":"Niculescu-Mizil A, Caruana R (2005) Predicting good probabilities with supervised learning. In: Proceedings of the 22nd international conference on machine learning. ACM, New York, ICML \u201905, pp 625\u201363. https:\/\/doi.org\/10.1145\/1102351.1102430","DOI":"10.1145\/1102351.1102430"},{"key":"7194_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103350","volume":"101","author":"T Pereira","year":"2020","unstructured":"Pereira T, Cardoso S, Guerreiro M, Mendon\u00e7a A, Madeira SC (2020) Targeting the uncertainty of predictions at patient-level using an ensemble of classifiers coupled with calibration methods, Venn-ABERS, and conformal predictors: a case study in ad. J Biomed Inf 101:103350. https:\/\/doi.org\/10.1016\/j.jbi.2019.103350","journal-title":"J Biomed Inf"},{"key":"7194_CR43","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.sigpro.2013.12.026","volume":"99","author":"MAF Pimentel","year":"2014","unstructured":"Pimentel MAF, Clifton DA, Clifton LA, Tarassenko L (2014) A review of novelty detection. Signal Process 99:215\u2013249","journal-title":"Signal Process"},{"key":"7194_CR44","unstructured":"Platt JC (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in large margin classifier. MIT Press, pp 61\u201374"},{"key":"7194_CR45","unstructured":"Pleiss G, Raghavan M, Wu F, Kleinberg J, Weinberger KQ (2017) On fairness and calibration. arXiv:1709.02012"},{"issue":"14","key":"7194_CR46","doi-asserted-by":"publisher","first-page":"2869","DOI":"10.1080\/00207540600654509","volume":"44","author":"D Raheja","year":"2006","unstructured":"Raheja D, Llinas J, Nagi R, Romanowski C (2006) Data fusion\/data mining-based architecture for condition-based maintenance. Int J Product Res 44(14):2869\u20132887. https:\/\/doi.org\/10.1080\/00207540600654509","journal-title":"Int J Product Res"},{"issue":"3","key":"7194_CR47","doi-asserted-by":"publisher","first-page":"1728","DOI":"10.1109\/TPWRD.2009.2016826","volume":"24","author":"A Rizzi","year":"2009","unstructured":"Rizzi A, Frattale Mascioli FM, Baldini F, Mazzetti C, Bartnikas R (2009) Genetic optimization of a PD diagnostic system for cable accessories. IEEE Trans Power Deliv 24(3):1728\u20131738","journal-title":"IEEE Trans Power Deliv"},{"issue":"2","key":"7194_CR48","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/TPAMI.2011.108","volume":"34","author":"C Rudin","year":"2012","unstructured":"Rudin C, Waltz D, Anderson RN, Boulanger A, Salleb-Aouissi A, Chow M, Dutta H, Gross PN, Huang B, Ierome S, Isaac DF, Kressner A, Passonneau RJ, Radeva A, Wu L (2012) Machine learning for the New York city power grid. IEEE Trans Pattern Anal Mach Intell 34(2):328\u2013345","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"7194_CR49","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1049\/iet-gtd.2008.0316","volume":"3","author":"SR Samantaray","year":"2009","unstructured":"Samantaray SR (2009) Decision tree-based fault zone identification and fault classification in flexible ac transmissions-based transmission line. IET Gener, Trans Distrib 3(5):425\u2013436","journal-title":"IET Gener, Trans Distrib"},{"issue":"3","key":"7194_CR50","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1023\/A:1007614523901","volume":"37","author":"RE Schapire","year":"1999","unstructured":"Schapire RE, Singer Y (1999) Improved boosting algorithms using confidence-rated predictions. Mach Learn 37(3):297\u2013336","journal-title":"Mach Learn"},{"key":"7194_CR51","doi-asserted-by":"crossref","unstructured":"Scott DW (1979) On optimal and data-based histograms. Biometrika 66(3):605\u2013610. http:\/\/www.jstor.org\/stable\/2335182","DOI":"10.1093\/biomet\/66.3.605"},{"key":"7194_CR52","doi-asserted-by":"crossref","unstructured":"Shahid N, Aleem SA, Naqvi IH, Zaffar N (2012) Support vector machine based fault detection classification in smart grids. In: 2012 IEEE Globecom workshops, pp 1526\u20131531","DOI":"10.1109\/GLOCOMW.2012.6477812"},{"key":"7194_CR53","doi-asserted-by":"publisher","DOI":"10.1007\/s40313-018-0406-7","author":"D Souza Pereira","year":"2018","unstructured":"Souza Pereira D, Almeida C, Kagan N (2018) Fault location in the smart grids context based on an evolutionary algorithm. J Control, Autom Electr Syst. https:\/\/doi.org\/10.1007\/s40313-018-0406-7","journal-title":"J Control, Autom Electr Syst."},{"key":"7194_CR54","doi-asserted-by":"publisher","first-page":"2567","DOI":"10.1002\/sim.1844","volume":"23","author":"E Steyerberg","year":"2004","unstructured":"Steyerberg E, Borsboom G, van Houwelingen JH, Eijkemans M, Habbema J (2004) Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Stat Med 23:2567\u201386. https:\/\/doi.org\/10.1002\/sim.1844","journal-title":"Stat Med"},{"issue":"4","key":"7194_CR55","doi-asserted-by":"publisher","first-page":"2053","DOI":"10.1109\/TPWRS.2004.836256","volume":"19","author":"J Sun","year":"2004","unstructured":"Sun J, Qin S-Y, Song Y-H (2004) Fault diagnosis of electric power systems based on fuzzy petri nets. IEEE Trans Power Syst 19(4):2053\u20132059","journal-title":"IEEE Trans Power Syst"},{"issue":"2","key":"7194_CR56","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1109\/TPWRD.2005.844307","volume":"20","author":"D Thukaram","year":"2005","unstructured":"Thukaram D, Khincha HP, Vijaynarasimha HP (2005) Artificial neural network and support vector machine approach for locating faults in radial distribution systems. IEEE Trans Power Deliv 20(2):710\u2013721","journal-title":"IEEE Trans Power Deliv"},{"key":"7194_CR57","doi-asserted-by":"crossref","unstructured":"Tokel HA, Halaseh RA, Alirezaei G, Mathar R (2018) A new approach for machine learning-based fault detection and classification in power systems. In: 2018 IEEE power energy society innovative smart grid technologies conference (ISGT), pp 1\u20135","DOI":"10.1109\/ISGT.2018.8403343"},{"key":"7194_CR58","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1466-7","author":"B Van Calster","year":"2019","unstructured":"Van Calster B, McLernon D, van Smeden M, Wynants L, Steyerberg E (2019) Calibration: the achilles heel of predictive analytics. BMC Med. https:\/\/doi.org\/10.1186\/s12916-019-1466-7","journal-title":"BMC Med"},{"key":"7194_CR59","unstructured":"Vovk V (2012) Venn predictors and isotonic regression. arXiv:1211.0025"},{"key":"7194_CR60","doi-asserted-by":"publisher","unstructured":"Vovk V, Gammerman A, Shafer G (2005) Algorithmic learning in a random world. Springer, Boston, pp 17\u201351. https:\/\/doi.org\/10.1007\/b106715","DOI":"10.1007\/b106715"},{"key":"7194_CR61","unstructured":"Vovk V, Petej I (2014) Venn-abers predictors. In: Proceedings of the 30th conference on uncertainty in artificial intelligence, UAI\u201914. AUAI Press, Arlington, pp 829\u2013838"},{"key":"7194_CR62","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970128","volume-title":"Spline models for observational data","author":"G Wahba","year":"1990","unstructured":"Wahba G (1990) Spline models for observational data. Society for Industrial and Applied Mathematics, Philadelphia"},{"key":"7194_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2017.10.008","author":"C Walsh","year":"2017","unstructured":"Walsh C, Sharman K, Hripcsak G (2017) Beyond discrimination: a comparison of calibration methods and clinical usefulness of predictive models of readmission risk. J Biomed Inf. https:\/\/doi.org\/10.1016\/j.jbi.2017.10.008","journal-title":"J Biomed Inf"},{"key":"7194_CR64","doi-asserted-by":"crossref","unstructured":"Wang Z, Zhao P (2009) Fault location recognition in transmission lines based on support vector machines. In: 2009 2nd IEEE international conference on computer science and information technology, pp 401\u2013404","DOI":"10.1109\/ICCSIT.2009.5234528"},{"key":"7194_CR65","doi-asserted-by":"publisher","unstructured":"Zadrozny B, Elkan C (2002) Transforming classifier scores into accurate multiclass probability estimates. In: Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining, KDD \u201902. ACM, New York, pp 694\u2013699. https:\/\/doi.org\/10.1145\/775047.775151","DOI":"10.1145\/775047.775151"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-022-07194-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-022-07194-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-022-07194-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T10:37:26Z","timestamp":1657363046000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-022-07194-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,23]]},"references-count":65,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["7194"],"URL":"https:\/\/doi.org\/10.1007\/s00500-022-07194-6","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,23]]},"assertion":[{"value":"24 April 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}