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The optimization of Artificial Neural Networks (ANNs) is an important task tothe success of using these models in real-world applications. The solutionsadopted to this task are expensive in general, involving trial-and-errorprocedures or expert knowledge which are not always available. In this work, weinvestigated the use of meta-learning to the optimization of ANNs.Meta-learning is a research field aiming to automatically acquiring knowledgewhich relates features of the learning problems to the performance of thelearning algorithms. The meta-learning techniques were originally proposed andevaluated to the algorithm selection problem and after to the optimization ofparameters for Support Vector Machines. However, meta-learning can be adoptedas a more general strategy to optimize ANN parameters, which motivates newefforts in this research direction. In the current work, we performed a casestudy using meta-learning to choose the number of hidden nodes for MLPnetworks, which is an important parameter to be defined aiming a good networksperformance. In our work, we generated a base of meta-examples associated to 93regression problems. Each meta-example was generated from a regression problemand stored: 16 features describing the problem (e.g., number of attributes andcorrelation among the problem attributes) and the best number of nodes for thisproblem, empirically chosen from a range of possible values. This set ofmeta-examples was given as input to a meta-learner which was able to predictthe best number of nodes for new problems based on their features. Theexperiments performed in this case study revealed satisfactory results.

Meta-aprendizado para otimizacao de parametros de redes neurais