Search Techniques for Automated Proposal of Data Mining Schemes

Authors: Roman Neruda
Publication year: 2016
Publication type: Konferenční příspěvek (mezinárodní konf.)
Periodical: Applied Computer Sciences in Engineering
Projects: Automated Knowledge and Plan Modeling for Autonomous Robots
Bibliographic citation: NERUDA, Roman. Search Techniques for Automated Proposal of Data Mining Schemes. In: FIGUEROA-GARCÍA, J.C., LÓPEZ-SANTANA, E.R., FERRO-ESCOBAR, R., eds. Applied Computer Sciences in Engineering. Cham: Springer, 2016, S. 84-90. Communications in Computer and Information Science, 657. ISBN 978-3-319-50879-5. ISSN 1865-0929
Dostupné z: doi:10.1007/978-3-319-50880-1_8
DOI: 10.1007/978-3-319-50880-1_8
ID: 470347

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.