{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T09:49:21Z","timestamp":1784886561008,"version":"3.55.0"},"reference-count":106,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T00:00:00Z","timestamp":1706659200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T00:00:00Z","timestamp":1706659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2012\/23114-9"],"award-info":[{"award-number":["2012\/23114-9"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2015\/03986-0"],"award-info":[{"award-number":["2015\/03986-0"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Talent Management in Autonomous Vehicle Control Technologies -- The Project is supported by the Hungarian Government and cofinanced by the European Social Fund","award":["EFOP-3.6.3-VEKOP-16-2017-00001"],"award-info":[{"award-number":["EFOP-3.6.3-VEKOP-16-2017-00001"]}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["409371\/2021-1"],"award-info":[{"award-number":["409371\/2021-1"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s10618-024-01002-5","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T19:03:23Z","timestamp":1706727803000},"page":"1364-1416","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms"],"prefix":"10.1007","volume":"38","author":[{"given":"Rafael","family":"Gomes Mantovani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom\u00e1\u0161","family":"Horv\u00e1th","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9 L. D.","family":"Rossi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ricardo","family":"Cerri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sylvio","family":"Barbon Junior","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joaquin","family":"Vanschoren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9 C. P. L. F. de","family":"Carvalho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"key":"1002_CR1","volume-title":"Support vector machines for pattern classification","author":"S Abe","year":"2005","unstructured":"Abe S (2005) Support vector machines for pattern classification. Springer, London"},{"key":"1002_CR2","first-page":"111:1","volume":"21","author":"E Alcoba\u00e7a","year":"2020","unstructured":"Alcoba\u00e7a E, Siqueira F, Rivolli A et al (2020) MFE: towards reproducible meta-feature extraction. J Mach Learn Res 21:111:1-111:5","journal-title":"J Mach Learn Res"},{"issue":"13","key":"1002_CR3","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.neucom.2006.03.004","volume":"70","author":"S Ali","year":"2006","unstructured":"Ali S, Smith-Miles KA (2006) A meta-learning approach to automatic kernel selection for support vector machines. Neurocomputing 70(13):173\u2013186","journal-title":"Neurocomputing"},{"key":"1002_CR4","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/978-1-4939-1384-8_10","volume-title":"Handbook of simulation optimization, international series in operations research & management science","author":"S Andradottir","year":"2015","unstructured":"Andradottir S (2015) A review of random search methods. In: Fu MC (ed) Handbook of simulation optimization, international series in operations research & management science, vol 216. Springer, New York, pp 277\u2013292"},{"key":"1002_CR5","unstructured":"Bache K, Lichman M (2013) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml"},{"key":"1002_CR6","unstructured":"Bardenet R, Brendel M, K\u00e9gl B et\u00a0al (2013) Collaborative hyperparameter tuning. In: Dasgupta S, Mcallester D (eds) Proceedings of the 30th international conference on machine learning (ICML-13), vol\u00a028. JMLR workshop and conference proceedings, pp 199\u2013207"},{"key":"1002_CR7","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.ins.2020.12.006","volume":"553","author":"VH Barella","year":"2021","unstructured":"Barella VH, Garcia LPF, de Souto MCP et al (2021) Assessing the data complexity of imbalanced datasets. Inf Sci 553:83\u2013109. https:\/\/doi.org\/10.1016\/j.ins.2020.12.006","journal-title":"Inf Sci"},{"issue":"3","key":"1002_CR8","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/TSMCC.2011.2157494","volume":"42","author":"R Barros","year":"2012","unstructured":"Barros R, Basgalupp M, de Carvalho A et al (2012) A survey of evolutionary algorithms for decision-tree induction. IEEE Trans Syst Man Cybern C Appl Rev 42(3):291\u2013312","journal-title":"IEEE Trans Syst Man Cybern C Appl Rev"},{"key":"1002_CR9","doi-asserted-by":"publisher","unstructured":"Barros RC, de Carvalho ACPLF, Freitas AA (2015) Automatic design of Decision-Tree induction algorithms. Springer Briefs in computer science. Springer, Berlin. https:\/\/doi.org\/10.1007\/978-3-319-14231-9","DOI":"10.1007\/978-3-319-14231-9"},{"key":"1002_CR10","unstructured":"Bartz E, Zaefferer M, Mersmann O et\u00a0al (2021) Experimental investigation and evaluation of model-based hyperparameter optimization. CoRR arXiv:abs\/2107.08761"},{"key":"1002_CR11","doi-asserted-by":"crossref","unstructured":"Ben-Hur A, Weston J (2010) A user\u2019s guide to support vector machines. In: Data mining techniques for the life sciences, methods in molecular biology, vol 609. Humana Press, pp 223\u2013239","DOI":"10.1007\/978-1-60327-241-4_13"},{"key":"1002_CR12","unstructured":"Bendtsen C (2012) pso: Particle Swarm Optimization. https:\/\/CRAN.R-project.org\/package=pso, r package version 1.0.3"},{"key":"1002_CR13","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281\u2013305","journal-title":"J Mach Learn Res"},{"key":"1002_CR14","unstructured":"Bergstra J, Yamins D, Cox DD (2013) Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. In: Proceedings of the 30th international conference on machine learning, pp 1\u20139"},{"key":"1002_CR15","unstructured":"Bergstra JS, Bardenet R, Bengio Y et\u00a0al (2011) Algorithms for hyper-parameter optimization. In: Shawe-Taylor J, Zemel RS, Bartlett PL, et\u00a0al (eds) Advances in neural information processing systems 24. Curran Associates, Inc., pp 2546\u20132554"},{"key":"1002_CR16","unstructured":"Berm\u00fadez-Chac\u00f3n R, Gonnet GH, Smith K (2015) Automatic problem-specific hyperparameter optimization and model selection for supervised machine learning: Technical Report. Tech. rep, Z\u00fcrich"},{"key":"1002_CR17","doi-asserted-by":"publisher","unstructured":"Birattari M, Yuan Z, Balaprakash P et\u00a0al (2010) F-race and iterated f-race: an overview. Springer, Berlin, pp 311\u2013336. https:\/\/doi.org\/10.1007\/978-3-642-02538-9_13","DOI":"10.1007\/978-3-642-02538-9_13"},{"issue":"170","key":"1002_CR18","first-page":"1","volume":"17","author":"B Bischl","year":"2016","unstructured":"Bischl B, Lang M, Kotthoff L et al (2016) mlr: machine learning in r. J Mach Learn Res 17(170):1\u20135","journal-title":"J Mach Learn Res"},{"key":"1002_CR19","doi-asserted-by":"crossref","unstructured":"Bischl B, Binder M, Lang M et\u00a0al (2023) Hyperparameter optimization: foundations, algorithms, best practices and open challenges. https:\/\/wires.onlinelibrary.wiley.com\/doi\/10.1002\/widm.1484","DOI":"10.1002\/widm.1484"},{"key":"1002_CR20","doi-asserted-by":"publisher","unstructured":"Blanco-Justicia A, Domingo-Ferrer J (2019) Machine learning explainability through comprehensible decision trees. In: Machine learning and knowledge extraction: third IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 international cross-domain conference, CD-MAKE 2019, Canterbury, UK, August 26\u201329, 2019, Proceedings. Springer, Berlin, pp 15\u201326. https:\/\/doi.org\/10.1007\/978-3-030-29726-8_2","DOI":"10.1007\/978-3-030-29726-8_2"},{"issue":"105","key":"1002_CR21","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1016\/j.knosys.2020.105532","volume":"194","author":"A Blanco-Justicia","year":"2020","unstructured":"Blanco-Justicia A, Domingo-Ferrer J, Mart\u00ednez S et al (2020) Machine learning explainability via microaggregation and shallow decision trees. Knowl Based Syst 194(105):532. https:\/\/doi.org\/10.1016\/j.knosys.2020.105532","journal-title":"Knowl Based Syst"},{"key":"1002_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-73263-1","volume-title":"Metalearning: applications to data mining","author":"P Brazdil","year":"2009","unstructured":"Brazdil P, Giraud-Carrier C, Soares C et al (2009) Metalearning: applications to data mining, 1st edn. Springer, Berlin","edition":"1"},{"key":"1002_CR23","volume-title":"Classification and regression trees","author":"L Breiman","year":"1984","unstructured":"Breiman L, Friedman J, Olshen R et al (1984) Classification and regression trees. Chapman & Hall (Wadsworth, Inc.), London"},{"key":"1002_CR24","doi-asserted-by":"crossref","unstructured":"Brodersen KH, Ong CS, Stephan KE et\u00a0al (2010) The balanced accuracy and its posterior distribution. In: Proceedings of the 2010 20th international conference on pattern recognition. IEEE Computer Society, pp 3121\u20133124","DOI":"10.1109\/ICPR.2010.764"},{"key":"1002_CR25","first-page":"2079","volume":"11","author":"GC Cawley","year":"2010","unstructured":"Cawley GC, Talbot NLC (2010) On over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res 11:2079\u20132107","journal-title":"J Mach Learn Res"},{"key":"1002_CR26","unstructured":"Clerc M (2012) Standard particle swarm optimization"},{"key":"1002_CR27","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"key":"1002_CR28","doi-asserted-by":"crossref","unstructured":"Eggensperger K, Hutter F, Hoos HH et\u00a0al (2015) Efficient benchmarking of hyperparameter optimizers via surrogates. In: Proceedings of the twenty-ninth AAAI conference on artificial intelligence. AAAI Press, AAAI\u201915, pp 1114\u20131120. http:\/\/dl.acm.org\/citation.cfm?id=2887007.2887162","DOI":"10.1609\/aaai.v29i1.9375"},{"issue":"2","key":"1002_CR29","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/j.cam.2005.09.009","volume":"196","author":"T Eitrich","year":"2006","unstructured":"Eitrich T, Lang B (2006) Efficient optimization of support vector machine learning parameters for unbalanced datasets. J Comp Appl Math 196(2):425\u2013436","journal-title":"J Comp Appl Math"},{"key":"1002_CR30","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1002\/(SICI)1526-4025(199910\/12)15:4<277::AID-ASMB393>3.0.CO;2-B","volume":"15","author":"F Esposito","year":"1999","unstructured":"Esposito F, Malerba D, Semeraro G et al (1999) The effects of pruning methods on the predictive accuracy of induced decision trees. Appl Stoch Models Bus Ind 15:277\u2013299","journal-title":"Appl Stoch Models Bus Ind"},{"key":"#cr-split#-1002_CR31.1","unstructured":"European Commission (2016) Regulation"},{"key":"#cr-split#-1002_CR31.2","unstructured":"(EU) 2016\/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95\/46\/EC (General Data Protection Regulation) (Text with EEA relevance). https:\/\/eur-lex.europa.eu\/eli\/reg\/2016\/679\/oj"},{"key":"1002_CR32","unstructured":"Falkner S, Klein A, Hutter F (2018) BOHB: robust and efficient hyperparameter optimization at scale. In: Dy J, Krause A (eds) Proceedings of the 35th international conference on Machine Learning, Proceedings of Machine Learning Research, vol\u00a080. PMLR, pp 1437\u20131446"},{"key":"1002_CR33","first-page":"3133","volume":"15","author":"M Fern\u00e1ndez-Delgado","year":"2014","unstructured":"Fern\u00e1ndez-Delgado M, Cernadas E, Barro S et al (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15:3133\u20133181","journal-title":"J Mach Learn Res"},{"key":"1002_CR34","unstructured":"Feurer M, Klein A, Eggensperger K et\u00a0al (2015a) Efficient and robust automated machine learning. In: Cortes C, Lawrence ND, Lee DD, et\u00a0al (eds) Advances in neural information processing systems 28. Curran Associates, Inc., pp 2944\u20132952"},{"key":"1002_CR35","doi-asserted-by":"crossref","unstructured":"Feurer M, Springenberg JT, Hutter F (2015b) Initializing Bayesian hyperparameter optimization via meta-learning. In: Proceedings of the twenty-ninth AAAI conference on artificial intelligence, AAAI\u201915. AAAI Press, pp 1128\u20131135. http:\/\/dl.acm.org\/citation.cfm?id=2887007.2887164","DOI":"10.1609\/aaai.v29i1.9354"},{"key":"1002_CR36","unstructured":"Feurer M, Eggensperger K, Falkner S et\u00a0al (2020) Auto-sklearn 2.0: hands-free automl via meta-learning. arXiv:2007.04074 [csLG]"},{"key":"1002_CR37","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1016\/j.knosys.2018.09.031","volume":"163","author":"LPF Garcia","year":"2019","unstructured":"Garcia LPF, Lehmann J, de Carvalho ACPLF et al (2019) New label noise injection methods for the evaluation of noise filters. Knowl Based Syst 163:693\u2013704. https:\/\/doi.org\/10.1016\/j.knosys.2018.09.031","journal-title":"Knowl Based Syst"},{"key":"1002_CR38","doi-asserted-by":"crossref","unstructured":"Gasc\u00f3n-Moreno J, Salcedo-Sanz S, Ortiz-Garc\u00eda EG et\u00a0al (2011) A binary-encoded tabu-list genetic algorithm for fast support vector regression hyper-parameters tuning. In: International conference on intelligent systems design and applications, pp 1253\u20131257","DOI":"10.1109\/ISDA.2011.6121831"},{"key":"1002_CR39","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1007\/978-3-030-67670-4_39","volume-title":"Machine learning and knowledge discovery in databases. Applied data science and demo track","author":"P Gijsbers","year":"2021","unstructured":"Gijsbers P, Vanschoren J (2021) Gama: a general automated machine learning assistant. In: Dong Y, Ifrim G, Mladeni\u0107 D et al (eds) Machine learning and knowledge discovery in databases. Applied data science and demo track. Springer, Cham, pp 560\u2013564"},{"key":"1002_CR40","volume-title":"Genetic algorithms in search, optimization and machine learning","author":"D Goldberg","year":"1989","unstructured":"Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison Wesley, London"},{"issue":"1","key":"1002_CR41","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neucom.2011.07.005","volume":"75","author":"TAF Gomes","year":"2012","unstructured":"Gomes TAF, Prud\u00eancio RBC, Soares C et al (2012) Combining meta-learning and search techniques to select parameters for support vector machines. Neurocomputing 75(1):3\u201313","journal-title":"Neurocomputing"},{"issue":"9","key":"1002_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v058.i09","volume":"58","author":"Y Gonzalez-Fernandez","year":"2014","unstructured":"Gonzalez-Fernandez Y, Soto M (2014) copulaedas: an R package for estimation of distribution algorithms based on copulas. J Stat Softw 58(9):1\u201334","journal-title":"J Stat Softw"},{"issue":"3","key":"1002_CR43","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.swevo.2011.08.003","volume":"1","author":"M Hauschild","year":"2011","unstructured":"Hauschild M, Pelikan M (2011) An introduction and survey of estimation of distribution algorithms. Swarm Evol Comput 1(3):111\u2013128","journal-title":"Swarm Evol Comput"},{"key":"1002_CR44","volume-title":"Neural networks: a comprehensive foundation","author":"S Haykin","year":"2007","unstructured":"Haykin S (2007) Neural networks: a comprehensive foundation, 3rd edn. Prentice-Hall, Upper Saddle River","edition":"3"},{"issue":"2","key":"1002_CR45","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s00180-008-0119-7","volume":"24","author":"K Hornik","year":"2009","unstructured":"Hornik K, Buchta C, Zeileis A (2009) Open-source machine learning: R meets Weka. Comput Stat 24(2):225\u2013232","journal-title":"Comput Stat"},{"issue":"3","key":"1002_CR46","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1198\/106186006X133933","volume":"15","author":"T Hothorn","year":"2006","unstructured":"Hothorn T, Hornik K, Zeileis A (2006) Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat 15(3):651\u2013674","journal-title":"J Comput Graph Stat"},{"issue":"1","key":"1002_CR47","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1186\/s12859-016-1228-x","volume":"17","author":"BF Huang","year":"2016","unstructured":"Huang BF, Boutros PC (2016) The parameter sensitivity of random forests. BMC Bioinform 17(1):331. https:\/\/doi.org\/10.1186\/s12859-016-1228-x","journal-title":"BMC Bioinform"},{"key":"1002_CR48","unstructured":"Hutter F, Hoos H, Leyton-Brown K (2014) An efficient approach for assessing hyperparameter importance. In: Proceedings of the 31th international conference on machine learning, ICML 2014, Beijing, China, 21\u201326 June 2014, pp 754\u2013762. http:\/\/jmlr.org\/proceedings\/papers\/v32\/hutter14.html"},{"key":"1002_CR49","series-title":"Lecture notes in computer science","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/978-3-662-45237-0_4","volume-title":"Computer information systems and industrial management","author":"D Jankowski","year":"2014","unstructured":"Jankowski D, Jackowski K (2014) Evolutionary algorithm for decision tree induction. In: Saeed K, Sn\u00e1\u0161el V (eds) Computer information systems and industrial management, vol 8838. Lecture notes in computer science. Springer, Berlin, pp 23\u201332"},{"key":"1002_CR50","unstructured":"Jed\u00a0Wing, Weston S, Williams A et\u00a0al (2016) caret: classification and regression training. https:\/\/CRAN.R-project.org\/package=caret, r package version 6.0-71"},{"key":"1002_CR51","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.neucom.2016.04.027","volume":"205","author":"J Kanda","year":"2016","unstructured":"Kanda J, de Carvalho A, Hruschka E et al (2016) Meta-learning to select the best meta-heuristic for the traveling salesman problem: a comparison of meta-features. Neurocomputing 205:393\u2013406. https:\/\/doi.org\/10.1016\/j.neucom.2016.04.027","journal-title":"Neurocomputing"},{"key":"1002_CR52","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Perth, Australia, pp 1942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"key":"1002_CR53","unstructured":"Kohavi R (1996) Scaling up the accuracy of Naive\u2013Bayes classifiers: a decision-tree hybrid. In: Second international conference on knowledge discovery and data mining, pp 202\u2013207"},{"key":"1002_CR54","first-page":"1","volume":"17","author":"L Kotthoff","year":"2016","unstructured":"Kotthoff L, Thornton C, Hoos HH et al (2016) Auto-weka 2.0: automatic model selection and hyperparameter optimization in weka. J Mach Learn Res 17:1\u20135","journal-title":"J Mach Learn Res"},{"issue":"1","key":"1002_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1758-2946-6-10","volume":"6","author":"D Krstajic","year":"2014","unstructured":"Krstajic D, Buturovic LJ, Leahy DE et al (2014) Cross-validation pitfalls when selecting and assessing regression and classification models. J Cheminform 6(1):1\u201315. https:\/\/doi.org\/10.1186\/1758-2946-6-10","journal-title":"J Cheminform"},{"issue":"1\u20132","key":"1002_CR56","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s10994-005-0466-3","volume":"95","author":"N Landwehr","year":"2005","unstructured":"Landwehr N, Hall M, Frank E (2005) Logistic model trees. Mach Learn 95(1\u20132):161\u2013205","journal-title":"Mach Learn"},{"issue":"1","key":"1002_CR57","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1080\/00949655.2014.929131","volume":"85","author":"M Lang","year":"2015","unstructured":"Lang M, Kotthaus H, Marwedel P et al (2015) Automatic model selection for high-dimensional survival analysis. J Stat Comput Simul 85(1):62\u201376. https:\/\/doi.org\/10.1080\/00949655.2014.929131","journal-title":"J Stat Comput Simul"},{"key":"1002_CR58","unstructured":"L\u00e9vesque JC, Gagn\u00e9 C, Sabourin R (2016) Bayesian hyperparameter optimization for ensemble learning. In: Proceedings of the thirty-second conference on uncertainty in artificial intelligence. AUAI Press, Arlington, Virginia, USA, UAI\u201916, pp 437\u2013446. http:\/\/dl.acm.org\/citation.cfm?id=3020948.3020994"},{"issue":"185","key":"1002_CR59","first-page":"1","volume":"18","author":"L Li","year":"2018","unstructured":"Li L, Jamieson K, DeSalvo G et al (2018) Hyperband: a novel bandit-based approach to hyperparameter optimization. J Mach Learn Res 18(185):1\u201352","journal-title":"J Mach Learn Res"},{"issue":"3","key":"1002_CR60","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw A, Wiener M (2002) Classification and regression by randomforest. R News 2(3):18\u201322","journal-title":"R News"},{"issue":"1","key":"1002_CR61","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s00500-011-0734-z","volume":"16","author":"SW Lin","year":"2012","unstructured":"Lin SW, Chen SC (2012) Parameter determination and feature selection for c4.5 algorithm using scatter search approach. Soft Comput 16(1):63\u201375. https:\/\/doi.org\/10.1007\/s00500-011-0734-z","journal-title":"Soft Comput"},{"issue":"3","key":"1002_CR62","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1111\/insr.12016","volume":"82","author":"WY Loh","year":"2014","unstructured":"Loh WY (2014) Fifty years of classification and regression trees. Int Stat Rev 82(3):329\u2013348","journal-title":"Int Stat Rev"},{"key":"1002_CR63","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.orp.2016.09.002","volume":"3","author":"M L\u00f3pez-Ib\u00e1\u00f1ez","year":"2016","unstructured":"L\u00f3pez-Ib\u00e1\u00f1ez M, Dubois-Lacoste J, C\u00e1ceres LP et al (2016) The irace package: iterated racing for automatic algorithm configuration. Oper Res Perspect 3:43\u201358. https:\/\/doi.org\/10.1016\/j.orp.2016.09.002","journal-title":"Oper Res Perspect"},{"key":"1002_CR64","unstructured":"Ma J (2012) Parameter tuning using Gaussian processes. Master\u2019s thesis, University of Waikato, New Zealand"},{"key":"1002_CR65","doi-asserted-by":"publisher","unstructured":"Mantovani RG, Horv\u00e1th T, Cerri R et\u00a0al (2016) Hyper-parameter tuning of a decision tree induction algorithm. In: 5th Brazilian conference on intelligent systems, BRACIS 2016, Recife, Brazil, October 9\u201312, 2016. IEEE Computer Society, pp 37\u201342. https:\/\/doi.org\/10.1109\/BRACIS.2016.018","DOI":"10.1109\/BRACIS.2016.018"},{"key":"1002_CR66","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.ins.2019.06.005","volume":"501","author":"RG Mantovani","year":"2019","unstructured":"Mantovani RG, Rossi AL, Alcoba\u00e7a E et al (2019) A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers. Inf Sci 501:193\u2013221. https:\/\/doi.org\/10.1016\/j.ins.2019.06.005","journal-title":"Inf Sci"},{"key":"1002_CR67","doi-asserted-by":"publisher","unstructured":"Massimo CM, Navarin N, Sperduti A (2016) Hyper-parameter tuning for graph kernels via multiple kernel learning. Springer, Cham, pp 214\u2013223. https:\/\/doi.org\/10.1007\/978-3-319-46672-9_25","DOI":"10.1007\/978-3-319-46672-9_25"},{"issue":"2","key":"1002_CR68","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1162\/EVCO\\_a_00137","volume":"23","author":"KL Mills","year":"2015","unstructured":"Mills KL, Filliben JJ, Haines AL (2015) Determining relative importance and effective settings for genetic algorithm control parameters. Evol Comput 23(2):309\u2013342. https:\/\/doi.org\/10.1162\/EVCO_a_00137","journal-title":"Evol Comput"},{"key":"1002_CR69","unstructured":"Miranda P, Silva R, Prud\u00eancio R (2014) Fine-tuning of support vector machine parameters using racing algorithms. In: Proceedings of the 22nd European symposium on artificial neural networks, computational intelligence and machine learning, ESANN 2014, pp 325\u2013330"},{"key":"1002_CR70","unstructured":"Molina MM, Luna JM, Romero C et\u00a0al (2012) Meta-learning approach for automatic parameter tuning: a case study with educational datasets. In: Proceedings of the 5th international conference on educational data mining, EDM 2012, pp 180\u2013183"},{"issue":"3","key":"1002_CR71","first-page":"199","volume":"13","author":"M Nakamura","year":"2014","unstructured":"Nakamura M, Otsuka A, Kimura H (2014) Automatic selection of classification algorithms for non-experts using meta-features. China-USA Bus Rev 13(3):199\u2013205","journal-title":"China-USA Bus Rev"},{"key":"1002_CR72","first-page":"787","volume-title":"Hyper-parameter tuning for support vector machines by estimation of distribution algorithms","author":"LC Padierna","year":"2017","unstructured":"Padierna LC, Carpio M, Rojas A et al (2017) Hyper-parameter tuning for support vector machines by estimation of distribution algorithms. Springer, Cham, pp 787\u2013800"},{"key":"1002_CR73","doi-asserted-by":"publisher","unstructured":"P\u00e9rez\u00a0C\u00e1ceres L, L\u00f3pez-Ib\u00e1\u00f1ez M, St\u00fctzle T (2014) An analysis of parameters of irace. Springer, Berlin, pp 37\u201348. https:\/\/doi.org\/10.1007\/978-3-662-44320-0_4","DOI":"10.1007\/978-3-662-44320-0_4"},{"key":"1002_CR74","doi-asserted-by":"publisher","unstructured":"Pil\u00e1t M, Neruda R (2013) Multi-objectivization and surrogate modelling for neural network hyper-parameters tuning. Springer, Berlin, pp 61\u201366. https:\/\/doi.org\/10.1007\/978-3-642-39678-6_11","DOI":"10.1007\/978-3-642-39678-6_11"},{"key":"1002_CR75","doi-asserted-by":"publisher","unstructured":"Podgorelec V, Karakatic S, Barros RC et\u00a0al (2015) Evolving balanced decision trees with a multi-population genetic algorithm. In: IEEE congress on evolutionary computation, CEC 2015, Sendai, Japan, May 25\u201328, 2015. IEEE, pp 54\u201361. https:\/\/doi.org\/10.1109\/CEC.2015.7256874","DOI":"10.1109\/CEC.2015.7256874"},{"key":"1002_CR76","first-page":"53:1","volume":"20","author":"P Probst","year":"2019","unstructured":"Probst P, Boulesteix A, Bischl B (2019) Tunability: importance of hyperparameters of machine learning algorithms. J Mach Learn Res 20:53:1-53:32","journal-title":"J Mach Learn Res"},{"key":"1002_CR77","unstructured":"Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Francisco"},{"key":"1002_CR78","series-title":"Lecture notes in computer science","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1007\/978-3-642-24455-1_25","volume-title":"KI 2011: advances in artificial intelligence","author":"M Reif","year":"2011","unstructured":"Reif M, Shafait F, Dengel A (2011) Prediction of classifier training time including parameter optimization. In: Bach J, Edelkamp S (eds) KI 2011: advances in artificial intelligence, vol 7006. Lecture notes in computer science. Springer, Berlin, pp 260\u2013271"},{"key":"1002_CR79","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1007\/s10994-012-5286-7","volume":"87","author":"M Reif","year":"2012","unstructured":"Reif M, Shafait F, Dengel A (2012) Meta-learning for evolutionary parameter optimization of classifiers. Mach Learn 87:357\u2013380","journal-title":"Mach Learn"},{"issue":"1","key":"1002_CR80","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s10044-012-0280-z","volume":"17","author":"M Reif","year":"2014","unstructured":"Reif M, Shafait F, Goldstein M et al (2014) Automatic classifier selection for non-experts. Pattern Anal Appl 17(1):83\u201396","journal-title":"Pattern Anal Appl"},{"key":"1002_CR81","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) Model-agnostic interpretability of machine learning. arXiv:1606.05386"},{"key":"1002_CR82","unstructured":"Ridd P, Giraud-Carrier C (2014) Using metalearning to predict when parameter optimization is likely to improve classification accuracy. In: Vanschoren J, Brazdil P, Soares C et\u00a0al (eds) Meta-learning and algorithm selection workshop at ECAI 2014, pp 18\u201323"},{"key":"1002_CR84","doi-asserted-by":"publisher","DOI":"10.1142\/9097","volume-title":"Data mining with decision trees: theory and applications","author":"L Rokach","year":"2014","unstructured":"Rokach L, Maimon O (2014) Data mining with decision trees: theory and applications, 2nd edn. World Scientific, River Edge","edition":"2"},{"key":"1002_CR85","doi-asserted-by":"crossref","unstructured":"Sabharwal A, Samulowitz H, Tesauro G (2016) Selecting near-optimal learners via incremental data allocation. In: Proceedings of the thirtieth AAAI conference on artificial intelligence. AAAI Press, AAAI\u201916, pp 2007\u20132015. http:\/\/dl.acm.org\/citation.cfm?id=3016100.3016179","DOI":"10.1609\/aaai.v30i1.10316"},{"key":"1002_CR86","doi-asserted-by":"crossref","unstructured":"Sanders S, Giraud-Carrier CG (2017) Informing the use of hyperparameter optimization through metalearning. In: 2017 IEEE International conference on data mining, ICDM 2017, New Orleans, LA, USA, November 18\u201321, 2017, pp 1051\u20131056","DOI":"10.1109\/ICDM.2017.137"},{"key":"1002_CR87","doi-asserted-by":"publisher","unstructured":"Schauerhuber M, Zeileis A, Meyer D et\u00a0al (2008) Benchmarking open-source tree learners in R\/RWeka. Springer, Berlin, pp 389\u2013396. https:\/\/doi.org\/10.1007\/978-3-540-78246-9_46","DOI":"10.1007\/978-3-540-78246-9_46"},{"key":"1002_CR88","doi-asserted-by":"publisher","unstructured":"Scrucca L (2013) Ga: a package for genetic algorithms in r. J Stat Softw 53(1):1\u201337. https:\/\/doi.org\/10.18637\/jss.v053.i04","DOI":"10.18637\/jss.v053.i04"},{"key":"1002_CR89","volume-title":"Evolutionary optimization algorithms","author":"D Simon","year":"2013","unstructured":"Simon D (2013) Evolutionary optimization algorithms, 1st edn. Wiley, New York","edition":"1"},{"key":"1002_CR90","unstructured":"Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. In: Pereira F, Burges C, Bottou L et\u00a0al (eds) Advances in neural information processing systems, vol 25. Curran Associates, Inc., pp 2951\u20132959"},{"issue":"3","key":"1002_CR91","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0033812","volume":"7","author":"G Stiglic","year":"2012","unstructured":"Stiglic G, Kocbek S, Pernek I et al (2012) Comprehensive decision tree models in bioinformatics. PLoS ONE 7(3):1\u201313. https:\/\/doi.org\/10.1371\/journal.pone.0033812","journal-title":"PLoS ONE"},{"issue":"1","key":"1002_CR92","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s10994-013-5387-y","volume":"93","author":"Q Sun","year":"2013","unstructured":"Sun Q, Pfahringer B (2013) Pairwise meta-rules for better meta-learning-based algorithm ranking. Mach Learn 93(1):141\u2013161. https:\/\/doi.org\/10.1007\/s10994-013-5387-y","journal-title":"Mach Learn"},{"key":"1002_CR93","doi-asserted-by":"publisher","unstructured":"Sureka A, Indukuri KV (2008) Using genetic algorithms for parameter optimization in building predictive data mining models. Springer, Berlin, pp 260\u2013271. https:\/\/doi.org\/10.1007\/978-3-540-88192-6_25","DOI":"10.1007\/978-3-540-88192-6_25"},{"key":"1002_CR94","unstructured":"Tan PN, Steinbach M, Kumar V (2005) Introduction to data mining, 1st edn. Addison-Wesley Longman Publishing Co., Inc, Boston"},{"key":"1002_CR95","doi-asserted-by":"publisher","unstructured":"Tantithamthavorn C, McIntosh S, Hassan AE et\u00a0al (2016) Automated parameter optimization of classification techniques for defect prediction models. In: Proceedings of the 38th international conference on software engineering. ACM, New York, NY, USA, ICSE\u201916, pp 321\u2013332. https:\/\/doi.org\/10.1145\/2884781.2884857","DOI":"10.1145\/2884781.2884857"},{"key":"1002_CR96","unstructured":"Therneau T, Atkinson B, Ripley B (2015) rpart: recursive partitioning and regression trees. https:\/\/CRAN.R-project.org\/package=rpart, r package version 4.1-10"},{"key":"1002_CR97","doi-asserted-by":"crossref","unstructured":"Thornton C, Hutter F, Hoos HH et\u00a0al (2013) Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings\u00a0of the KDD-2013, pp 847\u2013855","DOI":"10.1145\/2487575.2487629"},{"key":"1002_CR83","unstructured":"van Rijn JN, Hutter F (2017) An empirical study of hyperparameter importance across datasets. In: Proceedings of the international workshop on automatic selection, configuration and composition of machine learning algorithms co-located with the european conference on machine learning & principles and practice of knowledge discovery in databases, AutoML@PKDD\/ECML 2017, Skopje, Macedonia, September 22, 2017, pp 91\u201398. http:\/\/ceur-ws.org\/Vol-1998\/paper_09.pdf"},{"issue":"2","key":"1002_CR98","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/2641190.2641198","volume":"15","author":"J Vanschoren","year":"2014","unstructured":"Vanschoren J, van Rijn JN, Bischl B et al (2014) Openml: networked science in machine learning. SIGKDD Explor Newsl 15(2):49\u201360","journal-title":"SIGKDD Explor Newsl"},{"issue":"1","key":"1002_CR99","doi-asserted-by":"publisher","first-page":"113","DOI":"10.5335\/rbca.v12i1.10247","volume":"12","author":"CPR Vieira","year":"2020","unstructured":"Vieira CPR, Digiampietri LA (2020) A study about explainable articial intelligence: using decision tree to explain SVM. Revista Brasileira de Computa\u00e7\u00e3o Aplicada 12(1):113\u2013121. https:\/\/doi.org\/10.5335\/rbca.v12i1.10247","journal-title":"Revista Brasileira de Computa\u00e7\u00e3o Aplicada"},{"issue":"110","key":"1002_CR100","first-page":"1","volume":"17","author":"M Wainberg","year":"2016","unstructured":"Wainberg M, Alipanahi B, Frey BJ (2016) Are random forests truly the best classifiers? J Mach Learn Res 17(110):1\u20135","journal-title":"J Mach Learn Res"},{"key":"1002_CR101","unstructured":"Wang L, Feng M, Zhou B et\u00a0al (2015) Efficient hyper-parameter optimization for NLP applications. In: M\u00e0rquez L, Callison-Burch C, Su J et\u00a0al (eds) Proceedings of the 2015 conference on empirical methods in natural language processing, EMNLP 2015, Lisbon, Portugal, September 17\u201321, 2015. The Association for Computational Linguistics, pp 2112\u20132117. http:\/\/aclweb.org\/anthology\/D\/D15\/D15-1253.pdf"},{"key":"1002_CR102","volume-title":"Data mining: practical machine learning tools and techniques","author":"IH Witten","year":"2005","unstructured":"Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco","edition":"2"},{"key":"1002_CR103","doi-asserted-by":"publisher","DOI":"10.1201\/9781420089653","volume-title":"The top ten algorithms in data mining","author":"X Wu","year":"2009","unstructured":"Wu X, Kumar V (2009) The top ten algorithms in data mining, 1st edn. Chapman & Hall\/CRC, London","edition":"1"},{"key":"1002_CR104","volume-title":"Swarm intelligence and bio-inspired computation: theory and applications","author":"XS Yang","year":"2013","unstructured":"Yang XS, Cui Z, Xiao R et al (2013) Swarm intelligence and bio-inspired computation: theory and applications, 1st edn. Elsevier, Amsterdam","edition":"1"},{"key":"1002_CR105","doi-asserted-by":"publisher","unstructured":"Zambrano-Bigiarini M, Clerc M, Rojas R (2013) Standard particle swarm optimisation 2011 at CEC-2013: a baseline for future PSO improvements. In: Proceedings of the IEEE congress on evolutionary computation, CEC 2013, Cancun, Mexico, June 20\u201323, 2013. IEEE, pp 2337\u20132344. https:\/\/doi.org\/10.1109\/CEC.2013.6557848","DOI":"10.1109\/CEC.2013.6557848"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-024-01002-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-024-01002-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-024-01002-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T09:16:20Z","timestamp":1714814180000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-024-01002-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,31]]},"references-count":106,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["1002"],"URL":"https:\/\/doi.org\/10.1007\/s10618-024-01002-5","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,31]]},"assertion":[{"value":"26 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}