Abstract
When a real world system is described either by means of mathematical model or by any soft computing method the most important is to find out whether the model is of good quality, and for which configuration of input features the model is credible. Traditional methods restrict the credibility of model to areas of training data presence. These approaches are ineffective when non-relevant or redundant input features are present in the modeled system and for non-uniformly distributed data. Even for simple models, it is often hard to find out how credible the output is for any input vector. We propose a novel approach based on ensemble techniques that allows to estimate credibility of models. We experimentally derived an equation to estimate the credibility of models generated by Group of Adaptive Models Evolution (GAME) method for any configuration of input features.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Kordík, P., Šnorek, M. (2005). Ensemble Techniques for Credibility Estimation of GAME Models. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_21
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DOI: https://doi.org/10.1007/11550907_21
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