We describe an algorithm used to automatically create a hierarchy of documents. This hierarchy is quickly regenerated and allows for dynamic changes in a document database. We use natural language processing, text clustering with fuzzy adaptive parameter techniques, classification rule extraction and summarization to create and maintain this hierarchy.

This paper describes links between computational intelligence (CI), data mining and knowledge discovery. The generating elements of soft computing based data mining algorithms are defined where the extracted knowledge is represented by fuzzy rule-based expert systems. It is recognized that both model performance and interpretability are of major importance, and effort has to make to keep the re...

Using fuzzy/neural architectures to extract heuristic information from systems has received increasing attention. A number of fuzzy/neural architectures and knowledge extraction methods have been proposed. Knowledge extraction from systems where the existing knowledge limited is a difficult task. One of the reasons is that there is no ideal rulebase, which can be used to validate the extracted ...

Problem statement: Fusion weight tuning based on score reliability is imperative in order to ensure the performances of multibiometric systems are sustained. Approach: In this study, two variant of conditions i.e., different performances of individual subsystems and inconsistent quality of test samples are experimented to multibiometric systems. By applying multialgorithm scheme, two types of f...

There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...

A comprehensive methodology of extraction of optimal sets of logical rules using neural networks and global minimization procedures has been developed. Initial rules are extracted using density estimation neural networks with rectangular functions or multi-layered perceptron (MLP) networks trained with constrained backpropagation algorithm, transforming MLPs into simpler networks performing log...

this paper investigates existence and uniqueness results for the first order fuzzy integro-differential equations. then numerical results and error bound based on the left rectangular quadrature rule, trapezoidal rule and a hybrid of them are obtained. finally an example is given to illustrate the performance of the methods.

Control problems in the industry are dominated by nonlinear and time-varying behavior, many sensors that measure all kinds of variables, many loops and interaction among the control loops. The extraction of (fuzzy) information out of raw data is very important and contains saving potential for industrial applications. Major types of rule-based fuzzy models are described which are based on pre-p...

There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...

Recently, tuning the weights of the rules in Fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only  considers both accuracy and generalization criteria in a single objective fu...