[Bensusan, H. and Giraud-Carrier, C. (2000a). Casa batl´o is in passeig de gr´acia or how landmark performances can describe tasks, Proceedings of the ECML-00 Workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination, Barcelona, Spain, pp. 29-46.]Search in Google Scholar
[Bensusan, H. and Giraud-Carrier, C.G. (2000b). Discovering task neighbourhoods through landmark learning performances, Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery, Lyon, France, pp. 325-330.10.1007/3-540-45372-5_32]Search in Google Scholar
[Bensusan, H., Giraud-Carrier, C.G. and Kennedy, C.J. (2000). A higher-order approach to meta-learning, Proceedings of the International Conference on Inductive Logic Programming, London, UK.]Search in Google Scholar
[Bensusan, H. and Kalousis, A. (2001). Estimating the predictive accuracy of a classifier, in M. Bramer (Ed.), Principles of Data Mining. Undergraduate Topics in Computer Science, Springer, London, pp. 25-36.]Search in Google Scholar
[Bilalli, B., Abelló, A., Aluja-Banet, T. and Wrembel, R. (2016). Towards intelligent data analysis: The metadata challenge, Proceedings of the International Conference on Internet of Things and Big Data, Rome, Italy, pp. 331-338.]Search in Google Scholar
[Bilalli, B., Abelló, A., Aluja-Banet, T. and Wrembel, R. (2017). Intelligent assistance for data pre-processing, Computer Standards & Interfaces, DOI: 10.1016/j.csi.2017.05.004, (in press).10.1016/j.csi.2017.05.004()]Open DOISearch in Google Scholar
[Brazdil, P., Gama, J.A. and Henery, B. (1994). Characterizing the applicability of classification algorithms using meta-level learning, Proceedings of the European Conference on Machine Learning, Catania, Italy, pp. 83-102.]Search in Google Scholar
[Brazdil, P., Giraud-Carrier, C., Soares, C. and Vilalta, R. (2008). Metalearning: Applications to Data Mining, 1st Edn., Springer Publishing Company, Berlin.]Search in Google Scholar
[Castiello, C., Castellano, G. and Fanelli, A.M. (2005). Meta-data: Characterization of input features for meta-learning, Proceedings of the International Conference on Modeling Decisions for Artificial Intelligence, Tsukuba, Japan, pp. 457-468.]Search in Google Scholar
[Fayyad, U.M., Piatetsky-Shapiro, G. and Smyth, P. (1996). From data mining to knowledge discovery in databases, AI Magazine 17(3): 1-34.]Search in Google Scholar
[Fürnkranz, J. and Petrak, J. (2001). An evaluation of landmarking variants, Proceedings of the ECML/PKDD Workshop on Integrating Aspects of Data Mining, Decision Support and Meta-Learning, Freiburg, Germany, pp. 57-68.]Search in Google Scholar
[Giraud-Carrier, C. (2005). The data mining advisor: Meta-learning at the service of practitioners, Proceedings of the International Conference on Machine Learning and Applications, Los Angeles, CA, USA, pp. 113-119.]Search in Google Scholar
[Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components, Journal of Educational Psychology 24(6): 417-441.10.1037/h0071325]Open DOISearch in Google Scholar
[Kaiser, H.F. (1958). The varimax criterion for analytic rotation in factor analysis, Psychometrika 23(3): 187-200. 10.1007/BF02289233]Open DOISearch in Google Scholar
[Kalousis, A. and Hilario, M. (2001). Feature selection for meta-learning, Proceedings of the International Conference on Knowledge Discovery and Data Mining, Hong Kong, China, pp. 222-233.]Search in Google Scholar
[Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection, Proceedings of the International Joint Conference on Artificial Intelligence, Montr´eal, Qu´ebec, Canada, pp. 1137-1143.]Search in Google Scholar
[Lemke, C., Budka, M. and Gabrys, B. (2015). Metalearning: A survey of trends and technologies, Artificial Intelligence Review 44(1): 117-130.10.1007/s10462-013-9406-y445954326069389]Search in Google Scholar
[Michie, D., Spiegelhalter, D.J., Taylor, C.C. and Campbell, J. (Eds.) (1994). Machine Learning, Neural and Statistical Classification, Ellis Horwood, Chichester.]Search in Google Scholar
[Morchid, M., Dufour, R., Bousquet, P., Linarès, G. and Torres-Moreno, J. (2014). Feature selection using principal component analysis for massive retweet detection, Pattern Recognition Letters 49: 33-39.10.1016/j.patrec.2014.05.020]Open DOISearch in Google Scholar
[Peng, Y., Flach, P.A., Soares, C. and Brazdil, P. (2002). Improved dataset characterisation for meta-learning, Proceedings of the 5th International Conference on Discovery Science, L¨ubeck, Germany, pp. 141-152.]Search in Google Scholar
[Pfahringer, B., Bensusan, H. and Giraud-Carrier, C.G. (2000). Meta-learning by landmarking various learning algorithms, Proceedings of the 17th International Conference on Machine Learning, San Francisco, CA, USA, pp. 743-750.]Search in Google Scholar
[Reif, M., Shafait, F. and Dengel, A. (2012). Meta2-features: Providing meta-learners more information, 35th German Conference on Artificial Intelligence, Staarbr¨ucken, Germany.]Search in Google Scholar
[Reif, M., Shafait, F., Goldstein, M., Breuel, T. and Dengel, A. (2014). Automatic classifier selection for non-experts, Pattern Analysis and Applications 17(1): 83-96.10.1007/s10044-012-0280-z]Open DOISearch in Google Scholar
[Rendell, L., Seshu, R. and Tcheng, D. (1987). Layered concept-learning and dynamically-variable bias management, Proceedings of the International Joint Conference on Artificial Intelligence, Milan, Italy, pp. 308-314.]Search in Google Scholar
[Serban, F., Vanschoren, J., Kietz, J. and Bernstein, A. (2013). A survey of intelligent assistants for data analysis, ACM Computing Surveys 45(3): 31:1-31:35.10.1145/2480741.2480748]Open DOISearch in Google Scholar
[Sohn, S.Y. (1999). Meta analysis of classification algorithms for pattern recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 21(11): 1137-1144.10.1109/34.809107]Search in Google Scholar
[Todorovski, L., Brazdil, P. and Soares, C. (2000). Report on the experiments with feature selection in meta-level learning, Proceedings of the PKDD Workshop on Data Mining, Lyon, France, pp. 27-39.]Search in Google Scholar
[Todorovski, L. and Dzeroski, S. (1999). Experiments in meta-level learning with ILP, Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery, Prague, Czech Republic, pp. 98-106.]Search in Google Scholar
[Vanschoren, J., van Rijn, J.N., Bischl, B. and Torgo, L. (2014). OpenML: Networked science in machine learning, ACM SIGKDD Explorations Newsletter 15(2): 49-60.10.1145/2641190.2641198]Search in Google Scholar