In this paper, a different approach to extract the threshold value β of Variable Precision Rough Set (VPRS) applied to continuous information systems is presented. This study combines the Fuzzy Set and Rough Fuzzy Set (RFS) theories to determine the β value of VPRS. The β value was determined by the Fuzzy C-means and relevant Fuzzy theories, for the reason that errors of system classification c...

quality control plays an important role in increasing the product quality. fuzzy control charts are more sensitive than shewhart control chart. hence, the correct use of fuzzy control chart leads to producing better-quality products. this area is complex because it involves a large scope of industries, and information is not well organized. in this research, we provide a literature review of th...

In this work we study the behaviour of a Fuzzy Rule Based Classification System, and its relationship to a certain data complexity measures family. As Fuzzy Rule Based Classification System we have selected a recent proposal called Positive Definite Fuzzy Classifier, which is a Fuzzy System that uses Support Vector Machines for its training, obtaining accurate results and a low number of rules....

In this thesis, the usage of fuzzy classification algorithms in brain-computer interfaces (BCI) based on electroencephalography (EEG) is researched. We review the existing literature on BCI, the traditional crisp algorithms often used in BCI for classification, fuzzy classification algorithms and their application in BCI. A simple BCI system is implemented that allows the user to move a cursor ...

Since support vector machines for pattern classification are based on two-class classification problems, unclassifiable regions exist when extended to problems with more than two classes. In our previous work, to solve this problem, we developed fuzzy support vector machines for one-against-all and pairwise classifications, introducing membership functions. In this paper, for one-against-all cl...

This paper discusses a method for improving accuracy of fuzzy-rule-based classifiers using particle swarm optimization (PSO). Two different fuzzy classifiers are considered and optimized. The first classifier is based on Mamdani fuzzy inference system (M_PSO fuzzy classifier). The second classifier is based on TakagiSugeno fuzzy inference system (TS_PSO fuzzy classifier). The parameters of the ...

This paper introduces a new approach to classification which combines pairwise decomposition techniques from machine learning with ideas and tools from fuzzy preference modeling. The approach, called fuzzy relational classification, effectively reduces the problem of classification to a problem of decision making based on a fuzzy preference relation. It will be shown that, by decomposing such a...

Handling uncertainty and vagueness in real world becomes a necessity for developing intelligent and efficient systems. Based on the credibility theory, a fuzzy clustering approach that improves the classification accuracy is targeted by this work. This paper introduces a design of an efficient set of fuzzy rules that are inferred by a hybrid model of SOFM (Self Organized Features Maps) and FRAN...

In this paper we have proposed an evolutionary algorithm to induct fuzzy classification rules. The algorithm uses an ant colony optimization based local searcher to improve the quality of final fuzzy classification system. The proposed algorithm is performed on Intrusion Detection as a high-dimensional classification problem. Results show that the implemented evolutionary ACO-Based algorithm is...

In this paper, we aim to improve the classification of human non-speech sounds produced in a meeting-room environment by using concepts and tools from the fuzzy theory. Starting with an SVM-based baseline system, firstly a reduction of the number of features with the fuzzy measure is shown. And, secondly, a noticeable improvement of the classification performance is reported by combining the ou...