A comparative study of sample selection methods for classification

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Authors

Lutu, Patricia Elizabeth Nalwoga
Engelbrecht, Andries P.

Journal Title

Journal ISSN

Volume Title

Publisher

Computer Society of South Africa

Abstract

Sampling of large datasets for data mining is important for at least two reasons. The processing of large amounts of data results in increased computational complexity. The cost of this additional complexity may not be justifiable. On the other hand, the use of small samples results in fast and efficient computation for data mining algorithms. Statistical methods for obtaining sufficient samples from datasets for classification problems are discussed in this paper. Results are presented for an empirical study based on the use of sequential random sampling and sample evaluation using univariate hypothesis testing and an information theoretic measure. Comparisons are made between theoretical and empirical estimates.

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Keywords

Dataset sampling, Data analysis, Machine learning, Classification, Information measures

Sustainable Development Goals

Citation

Lutu, PEN & Engelbrecht, AP 2006, 'A comparative study of sample selection methods for classification', South African Computer Journal, issue 36, pp.69-85,[http://www.journals.co.za/ej/ejour_comp.html]