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Optimising Object Classification: Uncertain Reasoning based Analysis using CaRBS Systematic Search Algorithms

Beynon, Malcolm James 2008. Optimising Object Classification: Uncertain Reasoning based Analysis using CaRBS Systematic Search Algorithms. In: Vlahavas, Ioannis and Vrakas, Dimitris eds. Artificial Intelligence for Advanced Problem Solving, Hershey, PA: IGI Global, pp. 234-253. (10.4018/978-1-59904-705-8.ch009)

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Abstract

This chapter investigates the effectiveness of a number of objective functions used in conjunction with a novel technique to optimise the classification of objects based on a number of characteristic values, which may or may not be missing. The classification and ranking belief simplex (CaRBS) technique is based on Dempster-Shafer theory and, hence, operates in the presence of ignorance. The objective functions considered minimise the level of ambiguity and/or ignorance in the classification of companies to being either failed or not-failed. Further results are found when an incomplete version of the original data set is considered. The findings in this chapter demonstrate how techniques such as CaRBS, which operate in an uncertain reasoning based environment, offer a novel approach to object classification problem solving.

Item Type: Book Section
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: IGI Global
ISBN: 9781599047058
Related URLs:
Last Modified: 04 Jun 2017 03:41
URI: http://orca.cf.ac.uk/id/eprint/24788

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