The subject of this paper is the control of autonomous vehicles. A hierarchical approach is studied in the context of fuzzy systems and a programming language for the mid to low level control of autonomous vehicles is described. The language, called FDTL (Fuzzy Decision Tree Language), is based on a computational model that combines fuzzy rule based control with the hierarchical nature of decis...

To create a classifier using an associative classification algorithm, a complete set of class association rules (CARs) is obtained from the training dataset. Most generated rules, however , are either redundant or insignificant. They not only confuse end users during decision-making but also decrease the performance of the classification process. Thus, it is necessary to eliminate redundant or ...

i n troduction: cancer is a major cause of mortality in the modern world, and one of the most important health problems in societies. during recent years, research on cancer as a system biology disease is focused on molecular differences between cancer cells and healthy cells. most of the proposed methods for classifying cancer using gene expression data act as black boxes and lack biological i...

An approach to construct a new classifier called an intuitionistic fuzzy decision tree is presented. Well known benchmark data is used to analyze the performance of the classifier. The results are compared to some other popular classification algorithms. Finally, the classifier behavior is verified while solving a real-world classification problem.

In the recent years, a high number of fuzzy rule learning algorithms have been developed with the aim of building the Knowledge Base of Linguistic Fuzzy Rule Based Systems. In this context, it emerges the necessity of managing a flexible structure of the Knowledge Base with the aim of modeling the problems with a higher precision. In this work, we present a short overview on the Hierarchical Fu...

The issue of finding fuzzy models with an interpretability as good as possible without decreasing the accuracy is one of the main research topics on genetic fuzzy systems. When they are used to perform on-line reinforcement learning by means of Michigan-style fuzzy classifier systems, this issue becomes even more difficult. Indeed, rule generalization (description of state-action relationships ...

We examine the performance of a fuzzy genetics-based machine learning method for multidimensional pattern classification problems with continuous attributes. In our method, each fuzzy if-then rule is handled as an individual, and a fitness value is assigned to each rule. Thus, our method can be viewed as a classifier system. In this paper, we first describe fuzzy if-then rules and fuzzy reasoni...

The Internet makes it possible to share and manipulate a vast quantity of information efficiently and effectively, but the rapid and chaotic growth experienced by the Net has generated a poorly organized environment that hinders the sharing and mining of useful data. The need for meaningful web-page classification techniques is therefore becoming an urgent issue. This paper describes a novel ap...

In this paper, we address the problem of fuzzy rule-based pattern recognition with reject options. These options are made possible thanks to simple rules whose satisfaction level is expressed by the value of dedicated operators that aggregate degrees of typicality. Results obtained with the proposed classifier on articicial and real data are given.