A comparative study of sample selection methods for classification
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Date
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.
Description
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]