Imbalanced classification problems are attracting the attention of research community because they prevalent in real-world and impose extra difficulties for learning methods. Fuzzy rule-based systems have been applied to cope with these problems, mostly together sampling techniques. In this paper, we define a new fuzzy association classifier, named FARCI, tackle directly imbalanced problems. Ou...

This paper presents new pruning and learning methods for the fuzzy rule-based classifier. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the...

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. ...

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 ...