An exposition of feature selection and variable precision rough set analysis: application to financial data

Beynon, Malcolm James ORCID: https://orcid.org/0000-0002-5757-270X and Griffiths, Benjamin 2010. An exposition of feature selection and variable precision rough set analysis: application to financial data. Anbumani, K. and Nedunchezhian, R., eds. Soft Computing Applications for Database Technologies: Techniques and Issues, Hershey, PA.: IGI Global, pp. 193-213.

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Abstract

This chapter considers, and elucidates, the general methodology of rough set theory (RST), a nascent approach to rule based classification associated with soft computing. There are two parts of the elucidation undertaken in this chapter, firstly the levels of possible pre-processing necessary when undertaking an RST based analysis, and secondly the presentation of an analysis using variable precision rough sets (VPRS), a development on the original RST that allows for misclassification to exist in the constructed “if … then …” decision rules. Throughout the chapter, bespoke software underpins the pre-processing and VPRS analysis undertaken, including screenshots of its output. The problem of US bank credit ratings allows the pertinent demonstration of the soft computing approaches described throughout.

Item Type:	Book Section
Status:	Published
Schools:	Business (Including Economics)
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HD Industries. Land use. Labor H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management H Social Sciences > HF Commerce H Social Sciences > HG Finance
Additional Information:	Premier Reference Source
Publisher:	IGI Global
ISBN:	9781605668147
Related URLs:	Publisher Publisher
Last Modified:	19 Oct 2022 10:11
URI:	https://orca.cardiff.ac.uk/id/eprint/23535

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