Abstract
The undesired effects of data gathered from real world can be produced by the noise in the process, the bias of the sensors and the presence of hysteresis, among other uncertainty sources. In previous works the learning models using the so-called Low Quality Data (LQD) has been studied in order to analyze the way to represent the uncertainty. It makes use of genetic programming and the multiobjective simmulated annealing heuristic, which has been hybridized with genetic operators. The role of the tree generation methods when learning LQD was studied in that paper. The present work deals with the analysis of the generation methods relevance in depth and provides with statistical studies on the obtained results.
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Berzosa, A., Villar, J.R., Sedano, J., García-Tamargo, M., de la Cal, E. (2011). An Study of the Tree Generation Algorithms in Equation Based Model Learning with Low Quality Data. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_11
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DOI: https://doi.org/10.1007/978-3-642-21222-2_11
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