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 …

Many years have passed since Baesens et al. published their benchmarking study of
classification algorithms in credit scoring [Baesens, B., Van Gestel, T., Viaene, S …

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment,
and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from …

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 …

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 …

This work aims to connect two rarely combined research directions, ie, non-stationary data
stream classification and data analysis with skewed class distributions. We propose a novel …

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 …

Credit scoring aims to assess the risk associated with lending to individual consumers.
Recently, ensemble classification methodology has become popular in this field. However …