The overproduce-and-choose strategy, which is divided into the overproduction and
selection phases, has traditionally focused on finding the most accurate subset of classifiers …

Deep learning (DL) approaches are part of the machine learning (ML) subfield concerned
with the development of computational models to train artificial intelligence systems. DL …

This paper provides a review of multimodal biometric person authentication systems. The
paper begins with an introduction to biometrics, its advantages, disadvantages, and …

We propose a combined fusion-selection approach to classifier ensemble design. Each
classifier in the ensemble is replaced by a miniensemble of a pair of subclassifiers with a …

One popular approach employed to tackle classification problems in a static environment
consists in using a Dynamic Classifier Selection (DCS)-based method to select a custom …

During the 1990's many methods were proposed for combining multiple classifiers for a
single recognition task. With these methods, the focus of the field shifted from 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, we propose a novel ECG arrhythmia classification method using the
complementary features of Mixture of Experts (ME) and Negatively Correlated Learning …

Lenders, such as banks and credit card companies, use credit scoring models to evaluate
the potential risk posed by lending money to customers, and therefore to mitigate losses due …

State-of-the-art statistical NLP systems for a variety of tasks learn from labeled training data
that is often domain specific. However, there may be multiple domains or sources of interest …