{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T07:31:40Z","timestamp":1774251100360,"version":"3.50.1"},"reference-count":30,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2016,12,23]],"date-time":"2016-12-23T00:00:00Z","timestamp":1482451200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2017,1,30]]},"abstract":"<jats:p>The task of selecting the most suitable classification algorithm for each data set under analysis is still today a unsolved research problem. This paper therefore proposes a meta-learning based framework that helps both, practitioners and non-experts data mining users to make informed decisions about the goodness and suitability of each available technique for their data set at hand. In short, the framework is supported by an experimental database that is fed with the meta-features extracted from training data sets and the performance obtained by a set of classifiers applied over them, with the aim of building an algorithm recommender using regressors. This will allow the end-user to know, for a new unseen data set, the predicted accuracy of this set of algorithms ranked by this value. The experimentation performed and discussed in this paper is addressed to evaluate which meta-features are more significant and useful for characterising data sets with the end goal of building algorithm recommenders and to test the feasibility of these recommenders. The study is carried out on data sets from the educational arena, in particular, targeted to predict students\u2019 performance in e-learning courses.<\/jats:p>","DOI":"10.3233\/jifs-169141","type":"journal-article","created":{"date-parts":[[2016,12,23]],"date-time":"2016-12-23T17:41:22Z","timestamp":1482514882000},"page":"1449-1459","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["A meta-learning based framework for\u00a0building algorithm recommenders: An\u00a0application for educational arena"],"prefix":"10.1177","volume":"32","author":[{"given":"Diego","family":"Garc\u00eda-Saiz","sequence":"first","affiliation":[{"name":"Department of Computer Science and Electronics, University of Cantabria, Santander, Spain"}]},{"given":"Marta","family":"Zorrilla","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Electronics, University of Cantabria, Santander, Spain"}]}],"member":"179","published-online":{"date-parts":[[2016,12,23]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"406","article-title":"Model selection via meta-learning: A comparative study","author":"Kalousis A.","year":"2000","unstructured":"KalousisA. and HilarioM., Model selection via meta-learning: A comparative study, In Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence2000, pp. 406\u2013413.","journal-title":"Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.05.003"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.08.042"},{"key":"e_1_3_1_5_2","first-page":"743","volume-title":"Proceedings of the 17th International Conference on Machine Learning","author":"Pfahringer B.","year":"2000","unstructured":"PfahringerB., BensusanH. and Giraud-CarrierC., Metalearning by landmarking various learning algorithms, In Proceedings of the 17th International Conference on Machine Learning, Morgan Kaufmann, 2000, pp. 743\u2013750."},{"key":"e_1_3_1_6_2","volume-title":"Proceedings of the PKDD-00 Workshop on Data Mining, Decision Support","author":"K\u00f6pf C.","year":"2000","unstructured":"K\u00f6pfC., TaylorC. and KellerJ., Meta-analysis: From data characterisation for meta-learning to meta-regression, In Proceedings of the PKDD-00 Workshop on Data Mining, Decision Support, Meta-Learning and ILP, 2000."},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-013-9406-y"},{"key":"e_1_3_1_8_2","first-page":"268","article-title":"A meta-learning approach for recommending a subset of white-box classification algorithms for Moodle datasets","author":"Romero C.","year":"2013","unstructured":"RomeroC., OlmoJ.L. and VenturaS., A meta-learning approach for recommending a subset of white-box classification algorithms for Moodle datasets, In Proceedings of the 6th Int. Conference on Educational Data Mining, 2013, pp. 268\u2013271.","journal-title":"Proceedings of the 6th Int. Conference on Educational Data Mining"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2010.2053532"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1075"},{"key":"e_1_3_1_11_2","first-page":"25","article-title":"The supervised learning no-free-lunch theorems","author":"Wolpert D.H.","year":"2001","unstructured":"WolpertD.H., The supervised learning no-free-lunch theorems, In Proceedings of the 6th Online World Conference on Soft Computing in Industrial Applications, 2001, pp. 25\u201342.","journal-title":"Proceedings of the 6th Online World Conference on Soft Computing in Industrial Applications"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2327034"},{"key":"e_1_3_1_13_2","first-page":"1065","article-title":"Data complexity measures and nearest neighbor classifiers: A practical analysis for metalearning","author":"Cavalcanti G.","year":"2012","unstructured":"CavalcantiG., RenT. and ValeB., Data complexity measures and nearest neighbor classifiers: A practical analysis for metalearning, In Proceedings of the IEEE 24th International Conference on Tools with Artificial Intelligence, 2012, pp. 1065\u20131069.","journal-title":"Proceedings of the IEEE 24th International Conference on Tools with Artificial Intelligence"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1080\/18756891.2015.1113748"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-013-0700-4"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0065-2458(08)60520-3"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-011-5277-0"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-130599"},{"key":"e_1_3_1_19_2","first-page":"285","article-title":"Meta-learning: Can it be suitable to automatise the KDD process for the educational domain?","author":"Zorrilla M.E.","year":"2014","unstructured":"ZorrillaM.E. and Garc\u00eda-SaizD., Meta-learning: Can it be suitable to automatise the KDD process for the educational domain? In Proceedings of the Second International Conference on Rough Sets and Intelligent Systems Paradigms, 2014, pp. 285\u2013292.","journal-title":"Proceedings of the Second International Conference on Rough Sets and Intelligent Systems Paradigms"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24306-1_42"},{"key":"e_1_3_1_21_2","first-page":"180","article-title":"Building algorithm profiles for prior model selection in knowledge discovery systems","volume":"8","author":"Hilario M.","year":"2002","unstructured":"HilarioM. and KalousisA., Building algorithm profiles for prior model selection in knowledge discovery systems, Engineering Intelligent Systems8 (2002), 180\u2013183.","journal-title":"Engineering Intelligent Systems"},{"key":"e_1_3_1_22_2","first-page":"956","article-title":"Metalearning approach for automatic parameter tuning: A case study with educational datasets","author":"Molina M.M.","year":"2012","unstructured":"MolinaM.M., LunaJ.M., RomeroC. and VenturaS., Metalearning approach for automatic parameter tuning: A case study with educational datasets, In Proceedings of the 5th International Conference on Educational Data Mining, 2012, pp. 956\u2013961.","journal-title":"Proceedings of the 5th International Conference on Educational Data Mining"},{"key":"e_1_3_1_23_2","article-title":"Predicting classifier combinations","author":"Reif M.","year":"2013","unstructured":"ReifM., LeveringhausA., ShafaitF. and DengelA., Predicting classifier combinations, In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods, 2013.","journal-title":"Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-012-0280-z"},{"key":"e_1_3_1_25_2","first-page":"1","article-title":"On performance of meta-learning templates on different datasets","author":"Kord\u00edk P.","year":"2012","unstructured":"Kord\u00edkP. and Cern\u00fdJ., On performance of meta-learning templates on different datasets, In Proocedings of the IEEE World Congress on Computational Intelligence, 2012, pp. 1\u20137.","journal-title":"Proocedings of the IEEE World Congress on Computational Intelligence"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1019956318069"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsis.2015.02.001"},{"key":"e_1_3_1_28_2","first-page":"458","article-title":"Information-theoretic measures for meta-learning","author":"Segrera S.","year":"2008","unstructured":"SegreraS., PinhoJ. and MorenoM.N., Information-theoretic measures for meta-learning, In Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems, 2008, pp. 458\u2013465.","journal-title":"Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/240455.240464"},{"key":"e_1_3_1_30_2","unstructured":"HoT.K. Geometrical complexity of classification problems. CoRR cs. CV\/0402020 2004."},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-36182-0_14"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169141","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-169141","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169141","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T01:17:17Z","timestamp":1770513437000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-169141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,23]]},"references-count":30,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2017,1,30]]}},"alternative-id":["10.3233\/JIFS-169141"],"URL":"https:\/\/doi.org\/10.3233\/jifs-169141","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,12,23]]}}}