Abstract
Data mining schemes, or workflows, are collections of interconnected machine learning models, including preprocessing procedures, and ensembles methods combinations. The proposal of data mining schemes for a task at hand has always been a task for experienced data scientists. We will study generating and testing workflows by automated procedures. Two representations of data mining schemes are used in this paper – a linear one, and a one based on direct acyclic graphs. Efficient procedures for generating schemes are presented and evaluated by testing the generated schemes on real data.
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Acknowledgment
This work was supported by the Czech Science Foundation project no. P103-15-19877S. and the institutional support of the Institute of Computer Science, Czech Academy of Sciences RVO 67985807.
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Neruda, R. (2016). Search Techniques for Automated Proposal of Data Mining Schemes. In: Figueroa-García, J., López-Santana, E., Ferro-Escobar, R. (eds) Applied Computer Sciences in Engineering. WEA 2016. Communications in Computer and Information Science, vol 657. Springer, Cham. https://doi.org/10.1007/978-3-319-50880-1_8
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DOI: https://doi.org/10.1007/978-3-319-50880-1_8
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