Multiple classifier systems have been originally proposed for supervised classification tasks. In the five editions of MCS workshop, most of the papers have dealt with design methods and applications of supervised multiple classifier systems. Recently, the use of multiple classifier systems has been extended to unsupervised classification tasks. Despite its practical relevance, semi-supervised ...

Gene function prediction and protein function prediction are complex classification problems where the functional classes are structured according to a predefined hierarchy. To solve these problems, we propose an extended local hierarchical Naive Bayes classifier, where a binary classifier is built for each class in the hierarchy. The extension to conventional local approaches is that each clas...

brain mr images tissue segmentation is one of the most important parts of the clinical diagnostic tools. pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. moreove...

A description classifier organizes concepts and relations into a taxonomy based on the results of subsumption computations applied to pairs of relation definitions. Until now, description classifiers have only been designed to operate over definitions phrased in highly restricted subsets of the predicate calculus. This paper describes a classifier able to reason with definitions phrased in the ...

Introduction Breast cancer is the second cause of mortality among women. Early detection of it can enhance the chance of survival. Screening systems such as mammography cannot perfectly differentiate between patients and healthy individuals. Computer-aided diagnosis can help physicians make a more accurate diagnosis. Materials and Methods Regarding the importance of separating normal and abnorm...

Learning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. A new model of LCSs is introduced to classify realvalued data. The approach applies the cont...