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 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.FAPESPCNPqCAPESDAADIZKF AachenUniv Fed Sao Paulo, Inst Ciencia Tecnol, Unidade Parque Tecnol, BR-12247014 Sao Jose Dos Campos, SP, BrazilUniv Fed Pernambuco, Ctr Informat, BR-50740560 Recife, PE, BrazilRhein Westfal TH Aachen, IZKF Res Grp Bioinformat, Aachen, GermanyUniv Fed Sao Paulo, Inst Ciencia Tecnol, Unidade Parque Tecnol, BR-12247014 Sao Jose Dos Campos, SP, BrazilFAPESP: 2012/22608-8CNPq: 482222/2013-1CNPq: 308858/2014-0CNPq: 305611/2015-1Web of Scienc

A hybrid meta-learning architecture for multi-objective optimization of SVM parameters

Support Vector Machines (SVMs) have achieved a considerable attention due to their theoretical foundations and good empirical performance when compared to other learning algorithms in different applications. However, the SVM performance strongly depends on the adequate calibration of its parameters. In this work we proposed a hybrid multi-objective architecture which combines meta-learning (ML) with multi-objective particle swarm optimization algorithms for the SVM parameter selection problem. Given an input problem, the proposed architecture uses a ML technique to suggest an initial Pareto front of SVM configurations based on previous similar learning problems; the suggested Pareto front is then refined by a multi-objective optimization algorithm. In this combination, solutions provided by ML are possibly located in good regions in the search space. Hence, using a reduced number of successful candidates, the search process would converge faster and be less expensive. In the performed experiments, the proposed solution was compared to traditional multi-objective algorithms with random initialization, obtaining Pareto fronts with higher quality on a set of 100 classification problems.CNPqCAPESFAPES