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Search Techniques for Automated Proposal of Data Mining Schemes

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Applied Computer Sciences in Engineering (WEA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 657))

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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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Authors and Affiliations

  1. Institute of Computer Science Academy of Sciences of the Czech Republic, Pod Vodárenskou věží 2, 18207, Prague, Czech Republic

    Roman Neruda

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  1. Roman Neruda
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Correspondence to Roman Neruda .

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Editors and Affiliations

  1. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

  2. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Eduyn Ramiro López-Santana

  3. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Roberto Ferro-Escobar

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50879-5

  • Online ISBN: 978-3-319-50880-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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