Data complexity meta-features for regression problems
In meta-learning, classification problems can be described by a variety of features, including complexity measures. These measures allow capturing the complexity of the frontier that separates the classes. For regression problems, on the other hand, there is a lack of such type of measures. This pap...
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| Vydané v: | Machine learning Ročník 107; číslo 1; s. 209 - 246 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer US
01.01.2018
Springer Nature B.V |
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| ISSN: | 0885-6125, 1573-0565 |
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| Abstract | In meta-learning, classification problems can be described by a variety of features, including complexity measures. These measures allow capturing the complexity of the frontier that separates the classes. For regression problems, on the other hand, there is a lack of such type of measures. This paper presents and analyses measures devoted to estimate the complexity of the function that should fitted to the data in regression problems. As case studies, they are employed as meta-features in three meta-learning setups: (i) the first one predicts the regression function type of some synthetic datasets; (ii) the second one is designed to tune the parameter values of support vector regressors; and (iii) the third one aims to predict the performance of various regressors for a given dataset. The results show the suitability of the new measures to describe the regression datasets and their utility in the meta-learning tasks considered. In cases (ii) and (iii) the achieved results are also similar or better than those obtained by the use of classical meta-features in meta-learning. |
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| AbstractList | In meta-learning, classification problems can be described by a variety of features, including complexity measures. These measures allow capturing the complexity of the frontier that separates the classes. For regression problems, on the other hand, there is a lack of such type of measures. This paper presents and analyses measures devoted to estimate the complexity of the function that should fitted to the data in regression problems. As case studies, they are employed as meta-features in three meta-learning setups: (i) the first one predicts the regression function type of some synthetic datasets; (ii) the second one is designed to tune the parameter values of support vector regressors; and (iii) the third one aims to predict the performance of various regressors for a given dataset. The results show the suitability of the new measures to describe the regression datasets and their utility in the meta-learning tasks considered. In cases (ii) and (iii) the achieved results are also similar or better than those obtained by the use of classical meta-features in meta-learning. |
| Author | Maciel, Aron I. Prudêncio, Ricardo B. C. de Miranda, Péricles B. C. Costa, Ivan G. Lorena, Ana C. |
| Author_xml | – sequence: 1 givenname: Ana C. orcidid: 0000-0002-6140-571X surname: Lorena fullname: Lorena, Ana C. email: aclorena@unifesp.br organization: Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo – sequence: 2 givenname: Aron I. surname: Maciel fullname: Maciel, Aron I. organization: Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo – sequence: 3 givenname: Péricles B. C. surname: de Miranda fullname: de Miranda, Péricles B. C. organization: Centro de Informática, Universidade Federal de Pernambuco – sequence: 4 givenname: Ivan G. surname: Costa fullname: Costa, Ivan G. organization: IZKF Research Group Bioinformatics, RWTH Aachen University – sequence: 5 givenname: Ricardo B. C. surname: Prudêncio fullname: Prudêncio, Ricardo B. C. organization: Centro de Informática, Universidade Federal de Pernambuco |
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| Cites_doi | 10.1109/IJCNN.2016.7727369 10.1162/089976603321891855 10.1007/978-3-319-46128-1_13 10.1145/2487575.2487629 10.1007/978-3-642-31537-4_10 10.1109/ICTAI.2012.150 10.1007/s10115-016-1003-3 10.1109/ICDMW.2012.68 10.1016/j.neucom.2014.10.085 10.1016/j.ijforecast.2012.02.001 10.1109/34.990132 10.1016/j.neucom.2011.07.005 10.1023/B:MACH.0000015879.28004.9b 10.1109/TKDE.2014.2327034 10.1145/1141277.1141408 10.1007/s10710-013-9186-9 10.1016/j.neucom.2014.12.100 10.1016/j.neucom.2014.06.026 |
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| References_xml | – reference: GarciaLPde CarvalhoACLorenaACNoise detection in the meta-learning levelNeurocomputing2016176142510.1016/j.neucom.2014.12.100 – reference: SoaresCBrazdilPBKubaPA meta-learning method to select the kernel width in support vector regressionMachine Learning200454319520910.1023/B:MACH.0000015879.28004.9b1101.68083 – reference: BrazdilPGiraud-CarrierCSoaresCVilaltaRMeta-learning: Applications to data mining2008New YorkSpringer Science and Business Media1173.68625 – reference: LeyvaEGonzalezAPerezRA set of complexity measures designed for applying meta-learning to instance selectionIEEE Transactions on Knowledge and Data Engineering201527235436710.1109/TKDE.2014.2327034 – reference: PappaGLOchoaGHydeMRFreitasAAWoodwardJSwanJContrasting meta-learning and hyper-heuristic research: The role of evolutionary algorithmsGenetic Programming and Evolvable Machines201415133510.1007/s10710-013-9186-9 – reference: Soares, C., & Brazdil, P. B. (2006). Selecting parameters of SVM using meta-learning and kernel matrix-based meta-features. In: Proceedings of the 2006 ACM symposium on applied computing, ACM, SAC ’06, (pp. 564–568). – reference: Maciel, A. I., Costa, I. G., & Lorena, A. C. (2016). Measuring the complexity of regression problems. In: IEEE proceedings of the 2016 international conference on neural networks (in press). – reference: Thornton, C., Hutter, F., Hoos, H., & Leyton-Brown, K. (2013). Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 847–855). – reference: Soares, C. (2008). Development of metalearning systems for algorithm recommendation. In: P. Brazdil, C. Giraud-Carrier, C. Soares & R. Vilalta (Eds.), Meta-learning: applications to data mining (pp. 33–62). Springer. – reference: Amasyali, M., & Erson, O. (2009). A study of meta learning for regression. Tech. rep. 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