Towards a Framework for Modelling Variation in Automated Decision Support
Lead Research Organisation:
University of Nottingham
Department Name: School of Computer Science
Abstract
It has often been demonstrated, particularly in medical decision making, that groups of experts exhibit both intra-expert variability and inter-expert variability. The process of arriving at a single consensus decision, given the range of opinions obtained from the panel of experts, is a difficult task. In this proposal we aim to establish a framework that will enable us to create decision support systems that model this complex process. In order to achieve this ambitious goal, we propose to bring together, for the first time, three previously disparate strands of research: non-deterministic fuzzy systems - a very recently introduced paradigm in which intra-expert variation is modelled using fuzzy systems that vary over time, type-2 fuzzy models - in which the standard fuzzy membership values are 'blurred' to model the fact that such fuzzy membership values are themselves uncertain, and consensus models - in which a range of expert opinions are combined into a single overall collective decision. This will lay the foundations for an entirely new form of decision-making utilising fuzzy ensembles, which could make a significant contribution to future developments of fuzzy expert systems that model multiple experts reaching consensus.
Organisations
Publications
Zhou S
(2008)
Type-1 OWA operators for aggregating uncertain information with uncertain weights induced by type-2 linguistic quantifiers
in Fuzzy Sets and Systems
Shang-Ming Zhou
(2009)
On Constructing Parsimonious Type-2 Fuzzy Logic Systems via Influential Rule Selection
in IEEE Transactions on Fuzzy Systems
John R
(2008)
Automated Group Decision Support Systems Under Uncertainty: Trends and Future Research
in International Journal of Computational Intelligence Research
Garibaldi JM
(2012)
Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models.
in Journal of biomedical informatics