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Re-sampling based Data Mining using Rough Set Theory

Griffiths, Benjamin and Beynon, Malcolm James ORCID: 2008. Re-sampling based Data Mining using Rough Set Theory. Wang, John, ed. Data Mining and Warehousing: Concepts, Methodologies, Tools and Applications, Hershey, PA: IGI Global, pp. 3005-3026. (10.4018/978-1-59904-951-9.ch192)

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Predictive accuracy, as an estimation of a classifier’s future performance, has been studied for at least seventy years. With the advent of the modern computer era, techniques that may have been previously impractical are now calculable within a reasonable time frame. Within this chapter, three techniques of resampling, namely, leave-one-out, k-fold cross validation and bootstrapping; are investigated as methods of error rate estimation with application to variable precision rough set theory (VPRS). A prototype expert system is utilised to explore the nature of each resampling technique when VPRS is applied to an example dataset. The software produces a series of graphs and descriptive statistics, which are used to illustrate the characteristics of each technique with regards to VPRS, and comparisons are drawn between the results.

Item Type: Book Section
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: IGI Global
ISBN: 9781599049519
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Last Modified: 19 Oct 2022 10:38

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