Mixture of experts (ME) is one of the most popular and interesting combining methods, which
has great potential to improve performance in machine learning. ME is established based on …

Multimodal biometric systems consolidate the evidence presented by multiple biometric
sources and typically provide better recognition performance compared to systems based on …

The goal of ensemble regression is to combine several models in order to improve the
prediction accuracy in learning problems with a numerical target variable. The process of …

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 …

Class-imbalance learning is one of the most challenging problems in machine learning. As a
new and important direction in this field, multi-class imbalanced data classification has …

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 …

We present an ensemble method for concept drift that dynamically creates and removes
weighted experts in response to changes in performance. The method, dynamic weighted …

Reliable human authentication schemes are of paramount importance in our highly
networked society. Advances in biometrics help address the myriad of problems associated …

Ensemble approaches to classification and regression have attracted a great deal of interest
in recent years. These methods can be shown both theoretically and empirically to …

In unsupervised outlier ensembles, the absence of ground truth makes the combination of
base outlier detectors a challenging task. Specifically, existing parallel outlier ensembles …