Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Analysing incomplete consumer web data using the classification and ranking belief simplex (probabilistic reasoning and evolutionary computation)

Beynon, Malcolm James and Page, Kelly L. 2010. Analysing incomplete consumer web data using the classification and ranking belief simplex (probabilistic reasoning and evolutionary computation). In: Casillas, Jorge and Martínez-López, Francisco J. eds. Marketing information systems using soft computing, Studies in Fuzziness and Soft Computing, vol. 258. Berlin: Springer, pp. 447-473. (10.1007/978-3-642-15606-9_24)

Full text not available from this repository.

Abstract

Consumer attitudes, involvement and motives have long been identified as important determinates of decision making in classic models of consumer behaviour. Online consumer attitudes may differ depending on the level of web experience of the intended consumer. This chapter considers Classification and Ranking Belief Simplex (CaRBS) analyses of consumer web data, considering attitudes from consumers with different levels of web experience. The CaRBS technique is based on Probabilistic Reasoning (Dempster-Shafer theory) and Evolutionary Computation (Trignometric-Differential Evolution), two known components of soft computing. An important facet of the presented analyses is the ability of the CaRBS technique to analyse incomplete data, without the need for the missing values present to be managed in anyway. The chapter allows a pertinent demonstration of how soft computing, here using CaRBS, can offer the opportunity for realistic analysis, more realistic than traditional techniques.

Item Type: Book Section
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HA Statistics
H Social Sciences > HF Commerce
Publisher: Springer
ISBN: 9783642156052
Related URLs:
Last Modified: 04 Jun 2017 03:36
URI: http://orca.cf.ac.uk/id/eprint/23540

Citation Data

Cited 2 times in Google Scholar. View in Google Scholar

Actions (repository staff only)

Edit Item Edit Item