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

Abstract
The problem of learning rankings is receiving increased attention from several research communities. In this paper we empirically evaluate an adaptation of the algorithm of learning decision trees for rankings. Our experiments are carried out on some metalearning problems, which consist of relating characteristics of learning problems to the relative performance of learning algorithms. We obtain positive results which, somewhat surprisingly, indicate that the method predicts more accurately the top ranks. Copyright © 2008, Association for the Advancement of Artificial Intelligence.

Abstract

Abstract