Generalised Weighted Relevance Aggregation Operators for Hierarchical Fuzzy Signatures

Authors:
B. S. U. Mendis;T. D. Gedeon;J. Botzheim;L. T. Koczy
Affiliations:
Australian National University;Australian National University;Budapest University;University, Gyor, Hungary
Venue:
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Year:
2006

Citing 0
Cited 7

Eye gaze assistance for a game-like interactive task

International Journal of Computer Games Technology
Multi-layer fuzzy cognitive modeling using fuzzy signatures

FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Finding input sub-spaces for polymorphic fuzzy signatures

FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Fuzzy logic for cooperative robot communication

KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
Topological hierarchical tree using artificial ants

ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Multi-view gender classification using hierarchical classifiers structure

ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Complex Structured Decision Making Model: A hierarchical frame work for complex structured data

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Hierarchical Fuzzy Signatures are generalizations of the Vector Valued Fuzzy Set concept introduced in the 1970s. A crucial question in the Fuzzy Signature context is what kinds of aggregations are applicable for combining data with partly different substructures. Our earlier work introduced the Weighted Relevance Aggregation method to enhance the accuracy of the final results of calculations based on Hierarchical Fuzzy Signature Structures. In this paper, we further generalise the weights and the aggregation into a new operator called Weighted Relevance Aggregation Operator (WRAO). WRAO enhances the adaptability of the fuzzy signature model to different applications and simplifies the learning of fuzzy signature models from data. We also show the methodology of learning these aggregation operators from data.