Abstract
In this paper, we present an agent-based solution of meta-learning problem which focuses on optimization of data mining processes. We exploit the framework of computational multi-agent systems in which various meta-learning problems have been already studied, e.g. parameter-space search or simple method recommendation. In this paper, we examine the effect of data preprocessing for machine learning problems. We perform the set of experiments in the search-space of data mining processes which is constituted by combinations of preprocessing methods with classifiers. The optimization takes place with respect to two criteria — error-rate and model learning time, which are partially complementary. The results of the consistent search algorithm on a number of classification data-sets are shown and the advantage of automated preprocessing augmentation of method recommendation is demonstrated.
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Acknowledgments
O. Kazík has been supported by the Charles University Grant Agency project no. 629612 and by the SVV project no. 265314. R. Neruda was supported by Ministry of Education of the Czech Republic project no. LD13002.
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Kazík, O., Neruda, R. (2015). Data Mining Process Optimization in Computational Multi-agent Systems. In: Cao, L., et al. Agents and Data Mining Interaction. ADMI 2014. Lecture Notes in Computer Science(), vol 9145. Springer, Cham. https://doi.org/10.1007/978-3-319-20230-3_8
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DOI: https://doi.org/10.1007/978-3-319-20230-3_8
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