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 …

This work presents a literature review of multiple classifier systems based on the dynamic
selection of classifiers. First, it briefly reviews some basic concepts and definitions related to …

Dataset scaling, also known as normalization, is an essential preprocessing step in a
machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary …

A current focus of intense research in pattern classification is the combination of several
classifier systems, which can be built following either the same or different models and/or …

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

The conformal predictions framework is a recent development in machine learning that can
associate a reliable measure of confidence with a prediction in any real-world pattern …

In recent years, credit scoring has become an efficient tool that allows financial institutions to
differentiate their potential default borrowers. Accordingly, researchers have developed a …

Individual classification models are recently challenged by combined pattern recognition
systems, which often show better performance. In such systems the optimal set of classifiers …

Dynamic ensemble selection systems work by estimating the level of competence of each
classifier from a pool of classifiers. Only the most competent ones are selected to classify a …

Combination (ensembles) of classifiers is now a well established research line. It has been
observed that the predictive accuracy of a combination of independent classifiers excels that …