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Advances in Data Mining Knowledge Discovery and Applications
- 1.
SYSNO ASEP 0380642 Document Type M - Monograph Chapter R&D Document Type Monograph Chapter Title Selecting Representative Data Sets Author(s) Borovička, T. (CZ)
Jiřina jr., M. (CZ)
Kordík, P. (CZ)
Jiřina, Marcel (UIVT-O) SAI, RIDSource Title Advances in Data Mining Knowledge Discovery and Applications. - Rijeka : InTech, 2012 / Karahoca A. - ISBN 978-953-51-0748-4 Pages s. 43-70 Number of pages 28 s. Number of pages 418 Publication form Print - P Language eng - English Country HR - Croatia Keywords data selection ; classification ; class balancing ; sampling Subject RIV BB - Applied Statistics, Operational Research R&D Projects LG12020 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Institutional support UIVT-O - RVO:67985807 DOI https://doi.org/10.5772/50787 Annotation The aim of the chapter is to give an exhaustive overview and comparision of existing methods that deal with the methods of data selection and sampling. A general approach to the problem of optimal data selection (we could call it also splitting, dividing, sampling, ...) to training, testing and eventually validation sets is discussed. An overview of the methods together with their features, utilization, positives and negatives is given. Aptly presented algorithms of the methods are clearly summarized. Principles of selected methods are visualized in pictures and charts. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2013
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