We present an efficient method for extracting fuzzy classification rules from high dimensional data. A cluster estimation method called subtractive clustering is used to efficiently extract rules from a high dimensional feature space. A complementary search method can quickly identify the important input features from the resultant high dimensional fuzzy classifier, and thus provides the abilit...

Obtaining fuzzy rules from low quality data is a topic that has been recently formalized. This paper contains the first application of these principles to classification problems. We intend that the classifier proposed here serves as a baseline for future developments in the field. For that reason, we have extended a simple crisp genetic fuzzy classifier to imprecise data, paying special attent...

Design of a fuzzy rule based classifier is proposed. The performance of the classifier for multispectral satellite image classification is improved using DempsterShafer theory of evidence that exploits information of the neighboring pixels. The classifiers are tested rigorously with two known images and their performance are found to be better than the results available in the literature. We al...

This paper aims at analyzing a non-singleton fuzzy logic classifier (NSFLC) and assessing its ability to cope with uncertainties in pattern classification problems. The analysis demonstrate that the NSFLC has fuzzy classification boundary and noise suppression capability. These characteristics means that the NSFLC is particulary suitable for problems where the boundaries between classes is non-...

In this paper, we propose a novel scoring method for tumor prediction using an evolutionary fuzzy classifier which can provide accurate and interpretable information. The merits of the proposed method are threefold. 1) The score ranged in [0, 100] can further illustrate the degree of tumor status in contrast to the conventional tumor classifier. 2) The derived score system can be used as a tumo...

Although many classification methods take advantage of fuzzy sets theory, the same cannot be said for feature reduction methods. In this paper we explore ideas related to the use of fuzzy sets and we propose a novel fuzzy feature selection method tailored for the Regularized Generalized Eigenvalue Classifier (ReGEC). The method provides small and robust subsets of features that can be used for ...

In this paper we investigate the suitability of a fuzzy system as a classifier for imbalanced data problems. Primarily, the fuzzy model performance is evaluated on artificial data sets, generated with various levels of size, complexity and imbalance. It is investigated what combination of the three problematic issues makes the learning problem harder [4]. A theoretic analysis shows that for a f...

Traffic control in large cities is a difficult and non-trivial optimization problem. This paper presents a optimized classifier system using fuzzy logic for recognize and traffic signal control in a single intersection. Also this paper presents preliminary work into designing an intelligent local controller primarily for distributed traffic control systems by fuzzy logic. The idea is to use a c...

In this paper we study the problem of classification of textual web reports. We are specifically focused on situations in which structured information extracted from the reports is used for classification. We present an experimental classification system based on usage of third party linguistic analyzers, our previous work on web information extraction, and fuzzy inductive logic programming (fu...