Abstract
Spearman's rank correlation coefficient is not entirely suitable for measuring the correlation between two rankings in some applications because it treats all ranks equally. In 2000, Blest proposed an alternative measure of correlation that gives more importance to higher ranks but has some drawbacks. This paper proposes a weighted rank measure of correlation that weights the distance between two ranks using a linear function of those ranks, giving more importance to higher ranks than lower ones. It analyses its distribution and provides a table of critical values to test whether a given value of the coefficient is significantly different from zero. The paper also summarizes a number of applications for which the new measure is more suitable than Spearman's.

Abstract
When users have to choose a learning algorithm to induce a model for a given dataset, a common practice is to select an algorithm whose bias suits the data distribution. In real-world applications that produce data continuously this distribution may change over time. Thus, a learning algorithm with the adequate bias for a dataset may become unsuitable for new data following a different distribution. In this paper we present a meta-learning approach for periodic algorithm selection when data distribution may change over time. This approach exploits the knowledge obtained from the induction of models for different data chunks to improve the general predictive performance. It periodically applies a meta-classifier to predict the most appropriate learning algorithm for new unlabeled data. Characteristics extracted from past and incoming data, together with the predictive performance from different models, constitute the meta-data, which is used to induce this meta-classifier. Experimental results using data of a travel time prediction problem show its ability to improve the general performance of the learning system. The proposed approach can be applied to other time-changing tasks, since it is domain independent. © 2012 IEEE.

Abstract
Support Vector Machines (SVMs) have become a well succeeded technique due to the good performance it achieves on different learning problems. However, the SVM performance depends on adjustments of its parameters' values. The automatic SVM parameter selection is treated by many authors as an optimization problem whose goal is to find a suitable configuration of parameters for a given learning problem. This work performs a comparative study of combining Meta-Learning (ML) and Multi-Objective Particle Swarm Optimization (MOPSO) techniques for the SVM parameter selection problem. In this combination, configurations of parameters provided by ML are adopted as initial search points of the MOPSO techniques. Our hypothesis is that, starting the search with reasonable solutions will speed up the process performed by the MOPSO techniques. In our work, we implemented three MOPSO techniques applied to select two SVM parameters for classification. Our work's aim is to optimize the SVMs by seeking for configurations of parameters which maximize the success rate and minimize the number of support vectors (i.e., two objetive functions). In the experiments, the performance of the search algorithms using a traditional random initialization was compared to the performance achieved by initializing the search process using the ML suggestions. We verified that the combination of the techniques with ML obtained solutions with higher quality on a set of 40 classification problems. © 2012 IEEE.

Abstract
Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of a number of parameters, including for instance the kernel and the regularization parameters. In the current work, we propose the combination of Meta-Learning and search techniques to the problem of SVM parameter selection. Given an input problem, Meta-Learning is used to recommend SVM parameters based on well-succeeded parameters adopted in previous similar problems. The parameters returned by Meta-Learning are then used as initial search points to a search technique which will perform a further exploration of the parameter space. In this combination, we envisioned that the initial solutions provided by Meta-Learning are located in good regions in the search space (i.e. they are closer to the optimum solutions). Hence, the search technique would need to evaluate a lower number of candidate search points in order to find an adequate solution. In our work, we implemented a prototype in which Particle Swarm Optimization (PSO) was used to select the values of two SVM parameters for regression problems. In the performed experiments, the proposed solution was compared to a PSO with random initialization, obtaining better average results on a set of 40 regression problems. © 2010 IEEE.

Abstract
In this paper, a meta-learning approach is proposed to suggest the best optimization technique(s) for instances of the Traveling Salesman Problem. The problem is represented by a dataset where each example is associated with one of the instances. Thus, each example contains characteristics of an instance and is labeled with the name of the technique(s) that obtained the best solution for this instance. Since the best solution can be obtained by more than one technique, an example may have more than one label. Therefore, the meta-learning problem is addressed as a multi-label classification problem. Experiments with 535 instances of the problem were performed to evaluate the proposed approach, which produced promising results. © 2010 IEEE.

Abstract
Nowadays, microarray has become a fairly common tool for simultaneously inspecting the behavior of thousands of genes. Researchers have employed this technique to understand various biological phenomena. One straightforward use of such technology is identifying the class membership of the tissue samples based on their gene expression profiles. This task has been handled by a number of computational methods. In this paper, we provide a comprehensive evaluation of 7 commonly used algorithms over 6S publicly available gene expression datasets. The focus of the study was on comparing the performance of the algorithms in an efficient and sound manner, supporting the prospective users on how to proceed to choose the most adequate classification approach according to their investigation goals.