Data transformation in the evidential reasoning‐based decision making process

Abstract

This paper describes the application of an evidential reasoning (ER)-based decision making process to multiple-criteria decision making (MCDM) problems having both quantitative and qualitative criteria. The ER approach is based on the decision theory and the theory of evidence and it uses the concept of 'degree of belief ' to assess decision alternatives on each attribute. When faced with MCDM problems, evaluation and selection or ranking of alternatives appear to be both challenging and vital to arrive at a rational and robust decision. In the presence of both qualitative and quantitative evaluations in an MCDM problem, it is necessary, when using the ER-based decision making process, to transform or convert quantitative data into a belief structure using a number of grades so that the converted belief structure and the original quantitative data are equivalent in values or utilities. This paper suggests three scenarios for data transformation and examines how the ranking of decision alternatives is changed when different scenarios of data transformation are used. Ranking of UK universities using the ER approach is illustrated as an example.

A Novel Fault Detection Model Based on Atanassov’s Interval-Valued Intuitionistic Fuzzy Sets, Belief Rule Base and Evidential Reasoning

“…In [15], an approach based on rules and utility information was suggested, which can convert the quantitative information described by semantic level, interval-valued form, and qualitative information, into certitude structures. On the basis of previous research, Sonmez M [16] proposed three new transformation approaches for different situations.…”

Composition of probabilistic evaluations of preferences: a case of criteria applied to isolated and clustered options

“…There is always some amount of subjectivity in deriving preferences for any kind of attribute and the lack of information on the degree of uncertainty in classifications considerably impairs the use of such classifications. The imprecision of the evaluations and the need to take into account uncertainty in order to derive probabilistic conclusions can make it very difficult to combine multiple criteria (Balch et al, 1974; Selvanathan and Prasada Rao, 1994; Stewart, 2005; Sonmez, 2007).…”