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Assessment of Classification Models with Small Amounts of Data

Authors :
Ivan Rozman
Matjaž B. Jurič
Hannu Jaakkola
Boštjan Brumen
Tatjana Welzer
Apostolos N. Papadopoulos
Source :
Scopus-Elsevier
Publication Year :
2007
Publisher :
Vilnius University Press, 2007.

Abstract

One of the tasks of data mining is classification, which provides a mapping from attributes (observations) to pre-specified classes. Classification models are built by using underlying data. In principle, the models built with more data yield better results. However, the relationship between the available data and the performance is not well understood, except that the accuracy of a classification model has diminishing improvements as a function of data size. In this paper, we present an approach for an early assessment of the extracted knowledge (classification models) in the terms of performance (accuracy), based on the amount of data used. The assessment is based on the observation of the performance on smaller sample sizes. The solution is formally defined and used in an experiment. In experiments we show the correctness and utility of the approach.

Details

ISSN :
18228844 and 08684952
Volume :
18
Database :
OpenAIRE
Journal :
Informatica
Accession number :
edsair.doi.dedup.....6c1b8b98da6fa6f37b0fa3bfd5d6ee73
Full Text :
https://doi.org/10.15388/informatica.2007.181