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