Abstract
In the areas of Data Mining (DM) and Knowledge Discovery (KD), large variety of algorithms has been developed in the past decades, and the research is still ongoing. Data mining expertise is usually needed to deploy the algorithms available. Specifically, a process of interconnected actions referred to as knowledge flow (KF) needs to be assembled when the algorithms are to be applied to given data. In this paper, we propose an innovative evolutionary approach to automated KF synthesis and optimization. We demonstrate the evolutionary KF synthesis on the problem of classifier construction. Both preprocessing and machine learning actions are selected and configured by means of evolution to produce a model that fits very well for a given dataset.
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Rehorek, T., Kordik, P. (2013). A Soft Computing Approach to Knowledge Flow Synthesis and Optimization. In: Snášel, V., Abraham, A., Corchado, E. (eds) Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32922-7_3
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DOI: https://doi.org/10.1007/978-3-642-32922-7_3
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