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Search Techniques for Automated Proposal of Data Mining Schemes

  • Roman NerudaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 657)

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.

Keywords

Direct Acyclic Graph Description Logic Machine Learning Model Data Mining Process Computational Intelligence Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute of Computer Science Academy of Sciences of the Czech RepublicPragueCzech Republic

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