Abstract
Algae blooms are ecological events associated with extremely high abundance value of certain algae. These rare events have a strong impact in the river's ecosystem. In this context, the prediction of such events is of special importance. This paper addresses the problems that result from evaluating and comparing models at the prediction of rare extreme values using standard evaluation statistics. In this context, we describe a new evaluation statistic that we have proposed in Torgo and Ribeiro [Torgo, L., Ribeiro, R., 2006. Predicting rare extreme values. In: Ng, W, Kitsuregawa, M., Li, J., Chang, K. (Eds.), Proceedings of the loth Pacific-Asia Conference on Knowledge Discover and Data Mining (PAKDD'2006). Springer, pp. 816-820 (number 3918 in LNAI)], which can be used to identify the best models for predicting algae blooms. We apply this new statistic in a comparative study involving several models for predicting the abundance of different groups of phytoplankton in water samples collected in Douro River, Porto, Portugal. Results show that the proposed statistic identifies a variant of a Support Vector Machine as outperforming the other models that were tried in the prediction of algae blooms.

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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.