An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive diagnostic tests. Several frequently used techniques for designing classifiers of microarray data, such as support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low interpretabili...

In this paper, we present a novel method based on active contours for segmentation and fuzzy rule based classification of microscopic images of esophagus tissues obtained from the abnormal regions of human esophagus detected through endoscopy. This method is used for classification of Squamous Cell Carcinoma (SCC) of esophagus, namely, well differentiated (WD), moderately differentiated (MD), a...

fuzzy rule-based classification system (frbcs) is a popular machine learning technique for classification purposes. one of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. however many cases the minority classes are more important than the majority ones. in this paper, we have extended ...

Botnet threat has increased enormously with adoption of newer technologies like root kit, anti-antivirus modules etc. by the hackers. Emergence of botnets having distributed C & C structure that mimic P2P technologically, has made its detection and dismantling extremely difficult. However, numeric flow feature values of P2P botnet C & C traffic can be used to generate fuzzy rule-set which can t...

We report on experiments designed to highlight the strengths and weaknesses of an autonomous rule acquisition algorithm for the fuzzy controller of a simulated mobile robot. The algorithm is a Pittsburghstyle Fuzzy Classifier System. The highly cross-coupled and co-operative nature of fuzzy inference systems makes autonomous creation of an optimal rule-base a tough proposition. However, our res...

An approach to estimate the number of rules by spectral analysis of the training dataset has been recently proposed [1]. This work presents an analysis of such a method in high performance computing environment. Two approaches for parallel implementation of the method were studied considering the structure selection genetic algorithm and the spectral decomposition. The results show that both ap...

Software quality estimation based on measured attributes from previous similar products is an active field of research. Such estimation models must inevitably handle imprecision and uncertainty and hence soft computing techniques are gaining popularity. This paper presents a Fuzzy rule based classifier for software quality data and its performance is compared with Bayesian classifier. The fuzzy...

Fuzzy rule-based systems suffer from the so-called curse of dimensionality when applied to high complexity datasets, which consist of a large number of variables and/or examples. Fuzzy rule-based classifier ensembles have shown to be a good approach to deal with this kind of problems. In this contribution, we would like to take one step forward and extend this approach with two variants of rand...

This paper proposes a fuzzy methodology to translate the natural language descriptions of the TW3 method for bone age assessment into an automatic classifier. The classifier is built upon a modified version of a fuzzy ID3 decision tree. No large data records are needed to train the classifier, i.e., to find out the classification rules, since the classifier is built upon rules given by the TW3 ...

The concept of intelligently controlling the search process of gravitational search algorithm (GSA) is introduced to develop a novel data mining technique. The proposed method is called fuzzy GSA miner (FGSA-miner). At first a fuzzy controller is designed for adaptively controlling the gravitational coefficient and the number of effective objects, as two important parameters which play major ro...