Abstmct -The focus of this paper is semi-supervised learning in the context of pattern recognition. Semi-supervised learning (SSL) refers to the semi-supervised construction of clusters during the training phase of exemplar-based classifiers. Using artificially generated data sets we present experimental results of classifiers that follow the SSL paradigm and we show that, especially for diffic...

Many applications require classification of examples into one of several classes. A common way of designing such classifiers is to determine the class based on the outputs of several binary classifiers. We consider some of the most popular methods for combining the decisions of the binary classifiers, and improve existing bounds on the error rates of the combined classifier over the training se...

This work investigates the use of sampling methods in Genetic Programming (GP) to improve the classification accuracy in binary classification problems in which the datasets have a class imbalance. Class imbalance occurs when there are more data instances in one class than the other. As a consequence of this imbalance, when overall classification rate is used as the fitness function, as in stan...

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...

Many applications require classification of examples into one of several classes. A common way of designing such classifiers is to determine the class based on the outputs of several binary classifiers. We consider some of the most popular methods for combining the decisions of the binary classifiers, and improve existing bounds on the error rates of the combined classifier over the training se...

The problem of training classifiers only with target data arises in many applications where non-target data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as ...

Multiple classifier systems are used to improve the performance of base classifiers. One of the most important steps in the formation of multiple classifier systems is the integration process in which the base classifiers outputs are combined. The most commonly used classifiers outputs are class labels, the ranking list of possible classes or confidence levels. In this paper, we propose an inte...

Symbolic classifiers allow for solving classification task and provide the reason for the classifier decision. Such classifiers were studied by a large number of researchers and known under a number of names including tests, JSM-hypotheses, version spaces, emerging patterns, proper predictors of a target class, representative sets etc. Here we consider such classifiers with restriction on count...

The tree architecture has been employed to solve multi-class problems based on SVM. It is an alternative to the well known OVO/OVA strategies. Most of the tree base SVM classifiers try to split the multi-class space, mostly, by some clustering like algorithms into several binary partitions. One of the main drawbacks of this is that the natural class structure is not taken into account. Also the...