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.

Abstract
Several optimization methods can find good solutions for different instances of the Traveling Salesman Problem (TSP). Since there is no method that generates the best solution for all instances, the selection of the most promising method for a given TSP instance is a difficult task. This paper describes a meta-learning-based approach to select optimization methods for the TSP. Multilayer perceptron (MLP) networks are trained with TSP examples. These examples are described by a set of TSP characteristics and the cost of solutions obtained by a set of optimization methods. The trained MLP network model is then used to predict a ranking of these methods for a new TSP instance. Correlation measures are used to compare the predicted ranking with the ranking previously known. The obtained results suggest that the proposed approach is promising. © 2011 IEEE.

Abstract
Machine Learning techniques have been largely applied to the problem of class prediction in microarray data. Nevertheless, current approaches to select appropriate methods for such task often result unsatisfactory in many ways, instigating the need for the development of tools to automate the process. In this context, the authors introduce the use of metalearning in the specific domain of gene expression classification. Experiments with the KNN-ranking method for algorithm recommendation applied for 49 datasets yielded successful results. © 2008 IEEE.

Abstract
The performance of Artificial Neural Networks is largely influenced by the value of their parameters. Among these free parameters, one can mention those related with the network architecture, e.g., number of hidden neurons, number of hidden layers, activation function, and those associated with a learning algorithm, e.g., learning rate. Optimization techniques, often Genetic Algorithms, have been used to tune neural networks parameter values. Lately, other techniques inspired in Biology have been investigated. In this paper, we compare the influence of different bio-inspired optimization techniques on the accuracy obtained by the networks in the domain of gene expression analysis. The experimental results show the potential of use this techniques for parameter tuning of neural networks. © 2008 IEEE.

Abstract
The Support Vector Machine algorithm is sensitive to the choice of parameter settings. If these are not set correctly, the algorithm may have a substandard performance. Suggesting a good setting is thus an important problem. We propose a meta-learning methodology for this purpose and exploit information about the past performance of different settings. The methodology is applied to set the width of the Gaussian kernel. We carry out an extensive empirical evaluation, including comparisons with other methods (fixed default ranking; selection based on cross-validation and a heuristic method commonly used to set the width of the SVM kernel). We show that our methodology can select settings with low error while providing significant savings in time. Further work should be carried out to see how the methodology could be adapted to different parameter setting tasks.