Paper Info
Title
Selection of alternatives using fuzzy networks with rule base aggregation.
Abstract
This paper introduces a novel extension of the Technique for Ordering of Preference by Similarity to Ideal Solution (TOPSIS) method. The method is based on aggregation of rules with different linguistic of the output of fuzzy networks to solve multi-criteria decision-making problems whereby both benefit and cost criteria are presented as subsystems. Thus the decision maker evaluates the performance of each alternative for decision process and further observes the performance for both benefit and cost criteria. The aggregation sub-stage in a fuzzy system maps the fuzzy membership functions for all rules to an aggregated fuzzy membership function representing the overall output for the rules. This approach improves significantly the transparency of the TOPSIS methods, while ensuring high effectiveness in comparison to established approaches. To ensure practicality and effectiveness, the proposed method is further tested on portfolio selection problems. The ranking produced by the method is comparatively validated using Spearman rho rank correlation. The results show that the proposed method outperforms the existing TOPSIS approaches in term of ranking performance.
Year
DOI
Venue
2018
10.1016/j.fss.2017.05.027
Fuzzy Sets and Systems
Keywords
Field
DocType
TOPSIS,Fuzzy networks,Selection alternatives,Fuzzy sets,Interval type 2 fuzzy sets,Z-numbers,Stock selection,Spearman rho,Type 1 fuzzy sets
Data mining,Fuzzy classification,Defuzzification,Fuzzy set operations,Fuzzy logic,Fuzzy set,Artificial intelligence,TOPSIS,Fuzzy number,Membership function,Mathematics,Machine learning
Journal
Volume
ISSN
Citations 
341
0165-0114
0
PageRank 
References 
Authors
0.34
10
3
Name
Order
Citations
PageRank
Abdul Malek Yaakob1211.70
Alexander Gegov22917.65
Siti Fatimah Abdul Rahman320.71