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 …

Classifier ensembles are pattern recognition structures composed of a set of classification
algorithms (members), organized in a parallel way, and a combination method with the aim …

Abstract Machine learning algorithms have been effectively applied into various real world
tasks. However, it is difficult to provide high-quality machine learning solutions to …

In Dynamic Ensemble Selection (DES) techniques, only the most competent classifiers are
selected to classify a given query sample. Hence, the key issue in DES is how to estimate …

Facial expression recognition (FER) can assist the interaction between humans and
devices. The combination of FER and ensemble learning can usually improve final …

The ensemble pruning system is an effective machine learning framework that combines
several learners as experts to classify a test set. Generally, ensemble pruning systems aim …

Class imbalance refers to classification problems in which many more instances are
available for certain classes than for others. Such imbalanced datasets require special …

Ensemble pruning is effective for improving the accuracy of expression recognition. This
paper proposes a novel ensemble pruning algorithm called RTCRelief-F and applies it to …

Handwriting recognition technology allows recognizing a written text from a given data. The
recognition task can target letters, symbols, or words, and the input data can be a digital …

This study is based on EEG brain wave classification of a well-known dataset called the EEG
Brainwave Dataset. The dataset combines three classes such as positive, negative, and …