Abstract
Support Vector Machines (SVMs) have become a well succeed algorithm due to the good performance it achieves on different learning problems. However, to perform well the SVM formulation requires adjustments on its model. Avoiding the trial and error procedure, the automatic SVM parameter selection is a way to deal with this. The automatic parameter selection is commonly considered an optimization problem whose goal is to find suitable configuration of parameters which attends some learning problem. In the current work, we propose a study of the combination of Meta-learning (ML) with Particle Swarm Optimization (PSO) algorithms to optimize the SVM model, seeking for combinations of parameters which maximize the success rate of SVM. ML is used to recommend SVM parameters, to a given input problem, based on well-succeeded parameters adopted in previous similar problems. In this combination, initial solutions provided by ML are possibly located in good regions in the search space. Hence, using a reduced number of candidate search points, in the search process, to find an adequate solution, would be less expensive. In our work, we implemented five benchmarks PSO approaches applied to select two SVM parameters for classification. The experiments consist in comparing the performance of the search algorithms using a traditional random initialization and using ML suggestions as initial population. This research analysed the influence of meta-learning on convergence of the optimization algorithms, verifying that the combination of PSO techniques with ML obtained solutions with higher quality on a set of 40 classification problems.

Abstract
Support Vector Machines (SVMs) have become a well succeed technique due to the good performance it achieves on different learning problems. However, the performance depends on adjustments on its model. The automatic SVM parameter selection is a way to deal with this. This approach is considered an optimization problem whose goal is to find suitable configuration of parameters which attends some learning problem. This work proposes the use of Particle Swarm Optimization (PSO) to treat the SVM parameter selection problem. As the design of learning systems is inherently a multi-objective optimization problem, a multi-objective PSO (MOPSO) was used to maximize the success rate and minimize the number of support vectors of the model. Moreover, we propose the combination of Meta-Learning (ML) with MOPSO to the cited problem. ML is used to recommend SVM parameters, to a given input problem, based on well-succeeded parameters adopted in previous similar problems. In this combination, initial solutions provided by ML are possibly located in good regions in the search space. Hence, using a reduced number of candidate search points, the search process, to find an adequate solution, would be less expensive. We highlight that, the combination of search algorithms with ML was just studied in the single objective field and the use of MOPSO in this context has not been investigated. In our work, we implemented a prototype in which MOPSO was used to select the values of two SVM parameters for classification problems. In the performed experiments, the proposed solution (MOPSO using ML or Hybrid MOPSO) was compared to a MOPSO with random initialization, obtaining paretos with higher quality on a set of 40 classification problems.

Abstract
Support Vector Machine (SVM) is a supervised technique, which achieves good performance on different learning problems. However, adjustments on its model are essentials to the SVM work well. Optimization techniques have been used to automatize this process finding suitable configurations of parameters which attends some learning problems. This work utilizes Particle Swarm Optimization (PSO) applied to the SVM parameter selection problem. As the learning systems are essentially a multi-objective problem, a multi-objective PSO (MOPSO) was used to maximize the success rate and minimize the number of support vectors of the model. Nevertheless, we propose the combination of Meta-Learning (ML) with a modified MOPSO which uses the crowding distance mechanism (MOPSO-CDR). 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 our work, we implemented a prototype in which MOPSO-CDR was used to select the values of two SVM parameters for classification problems. In the performed experiments, the proposed solution (MOPSO-CDR using ML) was compared to the MOPSO-CDR with random initialization, obtaining pareto fronts with higher quality on a set of 40 classification problems.

Abstract
Different meta-heuristics (MHs) may find the best solutions for different traveling salesman problem (TSP) instances. The a priori selection of the best MH for a given instance is a difficult task. We address this task by using a meta-learning based approach, which ranks different MHs according to their expected performance. Our approach uses Multilayer Perceptrons (MLPs) for label ranking. It is tested on two different TSP scenarios, namely: re-visiting customers and visiting prospects. The experimental results show that: 1) MLPs can accurately predict MH rankings for TSP, 2) better TSP solutions can be obtained from a label ranking compared to multilabel classification approach, and 3) it is important to consider different TSP application scenarios when using meta-learning for MH selection.

Abstract
Purpose - The purpose of this paper is to investigate the applicability of meta-learning to the problem of algorithm recommendation for gene expression data classification. Design/methodology/approach - Meta-learning was used to provide a preference order of machine learning algorithms, based on their expected performances. Two approaches were considered for such: k-nearest neighbors and support vector machine-based ranking methods. They were applied to a set of 49 publicly available microarray datasets. The evaluation of the methods followed standard procedures suggested in the meta-learning literature. Findings - Empirical evidences show that both ranking methods produce more interesting suggestions for gene expression data classification than the baseline method. Although the rankings are more accurate, a significant difference in the performances of the top classifiers was not observed. Practical implications - As the experiments conducted in this paper suggest, the use of meta-learning approaches can provide an efficient data driven way to select algorithms for gene expression data classification. Originality/value - This paper reports contributions to the areas of meta-learning and gene expression data analysis. Regarding the former, it supports the claim that meta-learning can be suitably applied to problems of a specific domain, expanding its current practice. To the latter, it introduces a cost effective approach to better deal with classification tasks. © Emerald Group Publishing Limited.

Abstract