The concepts of robust classification and intelligently controlling the search process of genetic algorithm (GA) are introduced and integrated with a conventional genetic classifier for development of a new version of it, which is called Intelligent and Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision hyperplanes in the feature space. It is shown experime...

Recent researches have shown that ensembles of classifiers have more accuracy than a single classifier. Baging, boosting and error correcting output codes (ECOC) are most common ways for creating combination of classifiers. In this paper a new method for ensemble of classifiers has been introduced and performance of this method examined by applying to handwritten pen digits dataset. Experimenta...

An experimental evaluation of Bagging K-nearest neighbor classifiers (KNN) is performed. The goal is to investigate whether varying soft methods of aggregation would yield better results than Sum and Vote. We evaluate the performance of Sum, Product, MProduct, Minimum, Maximum, Median and Vote under varying parameters. The results over different training set sizes show minor improvement due to ...

In many fields, such as bioinformatics or multimedia, data may be described using different sets of features (or views) which carry either global or local information. Some learning tasks make use of these several views in order to improve overall predictive power of classifiers through fusion-based methods. Usually, these approaches rely on a weighted combination of classifiers (or selected de...

Vision-based applications designed for humanmachine interaction require fast and accurate hand detection. However, previous works on this field assume different constraints, like a limitation in the number of detected gestures, because hands are highly complex objects to locate. This paper presents an approach which changes the detection target without limiting the number of detected gestures. ...

This work presents a comparison of current research in the use of voting ensembles of classifiers in order to improve the accuracy of single classifiers and make the performance more robust against the difficulties that each individual classifier may have. Also, a number of combination rules are proposed. Different voting schemes are discussed and compared in order to study the performance of t...

This paper is on a graph clustering scheme inspired by ensemble learning. In short, the idea of ensemble learning is to learn several weak classifiers and use these weak classifiers to form a strong classifier. In this contribution, we use the generic procedure of ensemble learning and determine several weak graph clusterings (with respect to the objective function). From the partition given by...

Boosting is an established propositional learning method to promote the predictive accuracy of weak learning algorithms, and has achieved much empirical success. However, there have been relatively few efforts to apply boosting to Inductive Logic Programming (ILP) approaches. We investigate the use of boosting descriptive ILP systems, by proposing a novel algorithm for generating classification...

In this contribution we apply a method -called boostingfor constructing a classifier out of a set of (base or weak) classifiers for the discrimination of two groups of coffees (blends and monovarieties). The main idea of boosting is to produce a sequence of base classifiers that progressively concentrate on the hard patterns, i.e. those which are near to the classification boundary. Measurement...