Type-1 OWA operator based non-stationary fuzzy decision support systems for breast cancer treatments

Authors:
Shang-Ming Zhou;Jonathan M. Garibaldi;Francisco Chiclana;Robert I. John;Xiao-Ying Wang
Affiliations:
Centre for Computational Intelligence, Department of Informatics, DeMontfort University, Leicester, UK;Intelligent Modelling and Analysis Group, School of Computer Science and IT, University of Nottingham, Nottingham, UK;Centre for Computational Intelligence, Department of Informatics, DeMontfort University, Leicester, UK;Centre for Computational Intelligence, Department of Informatics, DeMontfort University, Leicester, UK;Intelligent Modelling and Analysis Group, School of Computer Science and IT, University of Nottingham, Nottingham, UK
Venue:
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Year:
2009

Citing 9
Cited 0

Fuzzy weighted averages and implementation of the extension principle

Fuzzy Sets and Systems
Fuzzy weighted average: The linear programming approach via charnes and cooper's rule

Fuzzy Sets and Systems
Fractional programming approach to fuzzy weighted average

Fuzzy Sets and Systems
Type-1 OWA operators for aggregating uncertain information with uncertain weights induced by type-2 linguistic quantifiers

Fuzzy Sets and Systems
Extracting Takagi-Sugeno Fuzzy Rules with Interpretable Submodels via Regularization of Linguistic Modifiers

IEEE Transactions on Knowledge and Data Engineering
Modeling uncertainty in clinical diagnosis using fuzzy logic

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Type-2 fuzzy sets made simple

IEEE Transactions on Fuzzy Systems
Uncertain Fuzzy Reasoning: A Case Study in Modelling Expert Decision Making

IEEE Transactions on Fuzzy Systems
Constructing L2-SVM-Based Fuzzy Classifiers in High-Dimensional Space With Automatic Model Selection and Fuzzy Rule Ranking

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a novel type-1 OWA based non-stationary fuzzy system is proposed, in which the type-1 OWA operator is used in the fuzzy inference process to aggregate the non-stationary fuzzy outputs. The advantage of non-stationary fuzzy sets lies in their ability to model expert's variations in automated decision support systems. The proposed scheme offers an opportunity to combine different uncertain objects with uncertain weights into an overall decision in the fuzzy inference process. The agreement achieved between the proposed fuzzy system and clinical expert decision in selecting optimal treatment plans is used to evaluate the performance of the method. The experimental results on post-operative breast cancer treatments have demonstrated that the proposed fuzzy system can effectively diagnose breast cancer treatment in decision supports.