Abstract
In this paper we introduce a new version of the machine learning algorithm, FDM2, based on a notion of the fuzzy derivative, which is mainly directed to handling large datasets. The main idea is to describe the influence of the change of one parameter on another. In this algorithm we generate sets of classification rules. We define a coefficient of significance for every single rule. This coefficient describes for a test example a degree of membership of the class predicted by the rule. A new example is classified into a class for which its total degree of membership is maximal. In this way the effect of a single non-informative rule having occurred by chance is decreased due the coefficient of significance. The fuzzy derivative method is mainly used to study systems with qualitative features, but it also can be used for systems with quantitative features. The algorithm is applied to classification problems and comparisons made with other techniques.
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Mamedov, M.A., Yearwood, J. (2002). An Induction Algorithm with Selection Significance Based on a Fuzzy Derivative. In: Abraham, A., Köppen, M. (eds) Hybrid Information Systems. Advances in Soft Computing, vol 14. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1782-9_17
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DOI: https://doi.org/10.1007/978-3-7908-1782-9_17
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