Abstract
There are various techniques for data mining and data analysis. Among them, hybrid approaches combining two or more algorithms gain importance as the complexity and dimension of real world data sets grows. In this paper, we present an application of evolutionary-fuzzy classification technique to data mining. Genetic programming is deployed to evolve a fuzzy classifier describing a set of anomalous patterns in data and the classifier is further used to prevent production of faulty products.
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Krömer, P., Snášel, V., Platoš, J. (2011). Learning Patterns from Data by an Evolutionary-Fuzzy Approach. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślȩzak, D. (eds) Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19644-7_14
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DOI: https://doi.org/10.1007/978-3-642-19644-7_14
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