Paper Info
Title
Providing PRTools with fuzzy rule-based classifiers
Abstract
This paper first reviews the state-of-the-art of fuzzy rule-based classifiers (FRBCs), then it discusses how to implement an FRBC under the Pattern Recognition Toolbox (PRTools), the de-facto standard toolbox for classification in Matlab. Such an implementation, called frbc, allows for a straightforward comparison of frbc with other classifiers already available under the PRTools. Furthermore, frbc can easily be used and combined with any other general-purpose function already available in PRTools. In this way, e.g., it becomes really easy to perform many types of feature selection, based on the accuracy achieved by frbc on the subset of features at hand. Another useful feature is the capability to export each FRBC generated by frbc as a standard Fuzzy Inference System (FIS) structure used within the Matlab Fuzzy Logic Toolbox (FLT): this allows comparisons/validations, visual inspection of the rule base, etc. In the experimental part we first assess the correctness of the implementation, by reproducing results existing in the literature. Then we show some examples of usage of frbc, combined with existing PRTools functions.
Year
DOI
Venue
2010
10.1109/FUZZY.2010.5584343
Fuzzy Systems
Keywords
Field
DocType
fuzzy logic,fuzzy reasoning,mathematics computing,pattern classification,Matlab Fuzzy Logic Toolbox,Pattern Recognition Toolbox,feature selection,fuzzy inference system structure,fuzzy rule-based classifier
Data mining,Feature selection,Computer science,Correctness,Toolbox,Fuzzy logic,Fuzzy set,Artificial intelligence,Statistical classification,Pattern matching,Machine learning,Fuzzy rule
Conference
ISSN
ISBN
Citations 
1098-7584
978-1-4244-6919-2
4
PageRank 
References 
Authors
0.45
13
3
Name
Order
Citations
PageRank
Marco Cococcioni119017.74
Eleonora D'Andrea261.49
Beatrice Lazzerini371545.56