Support vector machines (SVMs) have proved to be a good alternative compared to other machine learning techniques specifically for classification problems. However just like artificial neural networks (ANN), SVMs are also black box in nature because of its inability to explain the knowledge learnt in the process of training, which is very crucial in some applications like medical diagnosis, sec...

In reliability theory, the reliability measures contend the very important and depreciative role for any system analysis. Measurement of reliability measures is not easy due to ambiguity and vagueness which exist within reliability parameters. It is also very difficult to incorporate a large amount of uncertainty in well-established methodologies and techniques. However, fuzzy logic provides an...

An approach is developed to MR brain images segmentation, based on pixel classification using Fuzzy Rule Based system and Fuzzy Similarity measures. The cerebral images are segmented into gray matter, white matter, and cerebrospinal fluid (CSF). Image preprocessing was first done to improve the quality of brain MR images and reducing artifacts. The feature vector was selected to be the pixel an...

This paper presents our approach to the rule extraction problem from trained neural network. A method called REX is briefly described. REX acquires a set of fuzzy rules using an evolutionary algorithm. Evolutionary algorithm searches not only fuzzy rules, but also a description of fuzzy sets. The way of coding and evaluation process of an individual is presented. The method was tested using the...

The extraction of easily interpretable knowledge from the large amount of data measured in experiments is well desirable. This paper proposes a method to achieve this. A fuzzy rule system isjirst generated and optimized using evolution strategies. This fuzzy system is then converted to an RBF neural network to reJine the obtained knowledge. In order to extract understandable fuzzy rules from th...

An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean...

The purpose of the work described in this paper is to provide an intelligent intrusion detection system (IIDS) that uses two of the most popular data mining tasks, namely classification and association rules mining together for predicting different behaviors in networked computers. To achieve this, we propose a method based on iterative rule learning using a fuzzy rule-based genetic classifier....

Extracting fuzzy rules from data allows relationships in the data to be modeled by "if-then" rules that are easy to understand, verify, and extend. This paper presents methods for extracting fuzzy rules for both function approximation and pattern classification. The rule extraction methods are based on estimating clusters in the data; each cluster obtained corresponds to a fuzzy rule that relat...

The current paper presents an algorithm to build a fuzzy relational model from input-output data. The paper discuss the trade-oo between linguistic integrity and accuracy and propose an algorithm for rule extraction (AFRELI). The algorithm uses a routine named FuZion to merge consecutive membership functions and guaranteed the distinguishability between the fuzzy sets on each domain.

A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This net...