In this work we study the behaviour of a Fuzzy Rule Based Classification System, and its relationship to a certain data complexity measures family. As Fuzzy Rule Based Classification System we have selected a recent proposal called Positive Definite Fuzzy Classifier, which is a Fuzzy System that uses Support Vector Machines for its training, obtaining accurate results and a low number of rules....

Fuzzy memberships can be understood as coverage functions of random sets. This interpretation makes sense in the context of fuzzy rule learning: a random sets-based semantic of the linguistic labels is compatible with the use of fuzzy statistics for obtaining knowledge bases from data. In particular, in this paper we formulate the learning of a fuzzy rule based classifier as a problem of statis...

In this paper, 2-steps software using image processing and enhancement technologies is developed to obtain a scoliosis patient's spine pattern from 2D coronal X-Ray images without manual land marking. Then, a Rule-based Fuzzy classifier is implemented on those images to classify the spine patterns using the King-Moe classification approach.

A new approach is proposed for the data-based identification of transparent fuzzy rule-based classifiers. It is observed that fuzzy rule-based classifiers work in a similar manner as kernel function-based support vector machines (SVMs) since both model the input space by nonlinearly maps into a feature space where the decision can be easily made. Accordingly, trained SVM can be used for the con...

This paper describes a VLSI architecture for classification of multiand hyperspectral imagery using Fuzzy Logic with trapezoidal membership functions. The fuzzy classifier is implemented using a rule-based approach, where each class is defined as a set of sub rules. There is only one sub rule associated to each band within a class. Each sub rule is implemented as a dedicated parallel hardware. ...

A fuzzy rule-based system design concentrates on accuracy and interpretability of the system. Fuzzy decision tree method is proposed based on fuzzy RDBMS and rule generation based on C4.5 algorithm known as fuzzy rule generation system (FRGS) algorithm. A fuzzy decision tree is developed by first converting a medical application of heart relational database to fuzzy heart relational database an...

This contribution proposes a technique for Fuzzy Rule Based Classification Systems (FRBCSs) based on a multi-classifier approach using fuzzy preference relations for dealing with multi-class classification. The idea is to decompose the original data-set into binary classification problems using a pairwise coupling approach (confronting all pair of classes), and to obtain a fuzzy system for each...

The synthesis of genetics-based machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multi-variate non-linear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presented. A novel approach to genetics-based machine learning of fuzzy controllers, called a Pittsburgh ...

In this work, we conduct a study considering a fuzzy rule-based multiclassification system design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). This advanced method serves as the fuzzy classification rule learning algorithm to derive the component classifiers considering bagging and feature selection. We develop an exhaustive study on the potential of bagging and feature ...

In this paper, we propose a new feature selection method called class dependency based feature selection for dimensionality reduction of the macular disease dataset from pattern electroretinography (PERG) signals. In order to diagnosis of macular disease, we have used class dependency based feature selection as feature selection process, fuzzy weighted pre-processing as weighted process and dec...