Abstract
In the literature there exist several soft computing methods for building predictive models: neural network models, fuzzy models and probabilistic approaches. In this paper we are interested in the question which one of these approaches is likely to give best performance in practice. We study this problem empirically by selecting a set of typical models from the different model families, and by experimentally evaluating their predictive performance. For the evaluation, we use two real-world manufacturing datasets from a production plant of electrical machines. The models considered here include fuzzy rulebases, various neural network models and probabilistic finite mixtures. Our investigation indicates that all the methods can produce predictors that are accurate enough for practical purposes. Moreover, the results show that adding expert knowledge leads to improved predictive performance in the domain where such knowledge was available. In the domain where no expert knowledge was available, the probabilistic approach produced the best results.
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Koskimäki, E., Göös, J., Kontkanen, P., Myllymäki, P., Tirri, H. (1998). Comparing soft computing methods in prediction of manufacturing data. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_464
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DOI: https://doi.org/10.1007/3-540-64574-8_464
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