Abstract Multiple Classifier Systems (MCS) have been widely studied as an alternative for
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …

Many real-world classification tasks suffer from the class imbalanced problem, in which
some classes are highly underrepresented as compared to other classes. In this paper, we …

The concept of a classifier competence is fundamental to multiple classifier systems (MCSs).
In this study, a method for calculating the classifier competence is developed using a …

DESlib is an open-source python library providing the implementation of several dynamic
selection techniques. The library is divided into three modules:(i) dcs, containing the …

Dynamic ensemble selection (DES) techniques work by estimating the competence level of
each classifier from a pool of classifiers, and selecting only the most competent ones for the …

In this paper, a measure of competence based on random classification (MCR) for classifier
ensembles is presented. The measure selects dynamically (ie for each test example) a …

Building autonomous systems for deployment in the open world has been a longstanding
objective in both artificial intelligence and robotics. The open world, however, presents …

Machine learning methods in software engineering are becoming increasingly important as
they can improve quality and testing efficiency by constructing models to predict defects in …

One-class classification is among the most difficult areas of the contemporary machine
learning. The main problem lies in selecting the model for the data, as we do not have any …

In this paper the problem of recognition of the intended hand movements for the control of
bio-prosthetic hand is addressed. The proposed method is based on recognition of …