i n troduction: cancer is a major cause of mortality in the modern world, and one of the most important health problems in societies. during recent years, research on cancer as a system biology disease is focused on molecular differences between cancer cells and healthy cells. most of the proposed methods for classifying cancer using gene expression data act as black boxes and lack biological i...

Multi-label classification has attracted much attention in the machine learning community to address problem of assigning single samples more than one (not necessarily non-overlapping) class at same time. We propose an evolving multi-label fuzzy classifier (EFC-ML) which is able self-adapt and self-evolve its structure with new incoming incremental, single-pass manner. It based on a multi-outpu...

In this paper, the performance of a new fuzzy classifier, here called fuzzy β-NN, has been analyzed. The classifier classifies data according to the fuzzy membership values of the reference set inside the prespecified radius β. Members of the reference set outside the radius β have no influence on classification decision. The successful classification by the classifier depends on the parameters...

The Fuzzy Hyperline Segment Neural Network (FHLSNN) pattern classifier utilizes fuzzy set as pattern classes in which each fuzzy set is a union of fuzzy set hyperline segments. The Euclidean distance metric is used to compute the distances to decide the degree of membership function. In this paper, the use of other various distance metrics such as Manhattan, Squared Euclidean, Canberra and Cheb...

Our aim is to design a pattern classifier using fuzzy relational calculus (FRC) which was initially proposed by Pedrycz (1990). In the course of doing so, we first consider a particular interpretation of the multidimensional fuzzy implication (MFI) to represent our knowledge about the training data set. Subsequently, we introduce the notion of a fuzzy pattern vector to represent a population of...

One of the important issues in the design of fuzzy classifier is the formation of fuzzy if-then rules and the membership functions. This paper presents a Genetic Algorithm (GA) approach to obtain the optimal rule set and the membership function. To develop the fuzzy system the membership functions and rule set are encoded into the chromosome and evolved simultaneously using Genetic Algorithm. A...

Our aim is to design a pattern classifier using fuzzy relational calculus (FRC) which was initially proposed by Pedrycz (Pattern Recognition 23 (1/2), 121–146, 1990). In the course of doing so, we first consider a particular interpretation of the multidimensional fuzzy implication (MFI) to represent our knowledge about the training data set. Subsequently, we introduce the notion of a fuzzy patt...