fuzzy rules based classifier systems (FRBS) have gained more popularity in recent years. This classifier suffers from the problem of pattern space partitioning, to reach a compact set of rules that provides high classification power. In this paper, we propose an adaptive hierarchical fuzzy partitioning method based on tree decomposition. This decomposition is controlled by the grade of certaint...

Objective To develop a classifier that tackles the problem of determining the risk of a patient of suffering from a cardiovascular disease within the next ten years. The system has to provide both a diagnosis and an interpretable model explaining the decision. In this way, doctors are able to analyse the usefulness of the information given by the system. Methods Linguistic fuzzy rule-based clas...

Classifying web users in a personalised search setup is cumbersome due the very nature of dynamism in user browsing history. This fluctuating nature of user behaviour and user interest shall be well interpreted within a fuzzy setting. Prior to analysing user behaviour, nature of user interests has to be collected. This work proposes a fuzzy based user classification model to suit a personalised...

We describe an automatic approach for evaluating interpretability of fuzzy rule-based classifiers. The approach is based on the logical view of fuzzy rules, which are interpreted as rows in truth tables. These truth tables are subject of a minimization procedure based on a variant of the Quine-McCluskey algorithm. The minimized truth tables are used to build new fuzzy rules, which are compared ...

Missing value imputation is one of the biggest tasks of data pre-processing when performing data mining. Most medical datasets are usually incomplete. Simply removing the cases from the original datasets can bring more problems than solutions. A suitable method for missing value imputation can help to produce good quality datasets for better analysing clinical trials. In this paper we explore t...

In this paper we examine the performance of fuzzy rule-based systems with classification priority for medical diagnosis problems. The assumption in this paper is that a classification priority is given a priori for each class in a pattern classification problem. Our fuzzy rulebased system consists of a set of fuzzy if-then rules that are automatically generated from a set of given training patt...

A fuzzy logic based resource manager (RM) that will allocate resources distributed across many platforms is under development. The platforms will consist of ships, aircraft, etc. The resources will be various sensors: ESM, RADAR, IFF, and communications. The RM will allow codification of military expertise in a simple mathematical formalism known as the fuzzy decision tree. The fuzzy decision t...

An important issue in the design of FRBS is the formation of fuzzy if-then rules and the membership functions. This paper presents a Mixed Genetic Algorithm (MGA) approach to obtain the optimal rule set and the membership function of the fuzzy classifier. While applying genetic algorithm for fuzzy classifier design, the membership functions are represented as real numbers and the fuzzy rules ar...

Fuzzy classification has become of great interest because of its ability to utilize simple linguistically interpretable rules and has overcome the limitations of symbolic or crisp rule based classifiers. This paper introduces an extension to fuzzy classifier: a neutrosophic classifier, which would utilize neutrosophic logic for its working. Neutrosophic logic is a generalized logic that is capa...

predicting different behaviors in computer networks is the subject of many data mining researches. providing a balanced intrusion detection system (ids) that directly addresses the trade-off between the ability to detect new attack types and providing low false detection rate is a fundamental challenge. many of the proposed methods perform well in one of the two aspects, and concentrate on a su...