Paper Info
Title
The Application Of Generalised Fuzzy Rules To Machine Learning And Automated Knowledge Discovery
Abstract
Notions of generalised fuzzy conditional and equivalence rules relative to a combination function are introduced and a framework for reasoning with such rules is described. The applicability of this framework to machine learning and knowledge discovery problems is demonstrated. Methods for the automatic generation of two particular types of generalised rule are proposed. The two rule forms considered are rules with weighted AND/OR combination functions, as suggested by Zimmerman and Zysno, and evidential logic equivalence rules as defined by Baldwin. The process of generating rule bases is divided into the problem of generating fuzzy sets from data and that of finding combination functions to optimise the performance of the system given these fuzzy sets. For the former problem a mass assignment based approach is adopted and for the latter semantic discrimination analysis is used in conjunction with customised optimisation algorithms. The potential of rule bases of both forms is illustrated by their application to a number of model and real world machine learning problems.
Year
DOI
Venue
1998
10.1142/S0218488598000367
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS
Keywords
Field
DocType
generalised fuzzy rule, combination function, aggregation operator, mass assignment theory, machine learning, knowledge discovery
Neuro-fuzzy,Fuzzy classification,Defuzzification,Fuzzy set operations,Fuzzy set,Artificial intelligence,Type-2 fuzzy sets and systems,Fuzzy associative matrix,Fuzzy number,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
6
5
0218-4885
Citations 
PageRank 
References 
10
1.79
0
Authors
3
Name
Order
Citations
PageRank
James F. Baldwin1254.57
Jonathan Lawry2101.79
Trevor P. Martin313426.98