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 …

Modeling and forecasting are impressive and active research areas, which have been
widely used in diverse theoretical and practical applications, successfully. Accuracy is the …

In recent years, a forward-looking subfield of machine learning has emerged with important
applications in a variety of scientific fields. Semi-supervised learning is increasingly being …

Dynamic ensemble selection (DES) systems work by estimating the level of competence of
each classifier from a pool of classifiers and selecting the most competent ones for the …

Abstract Dynamic Ensemble Selection (DES) techniques aim to select one or more
competent classifiers for the classification of each new test sample. Most DES techniques …

In online advertising, the user-clicks dataset based fraudulent publishers' classification
models exhibit poor performance due to high skewness in class distribution of the …

The abundant spectral information of hyperspectral imagery makes it suitable for the
classification of land cover types. However, the high dimensionality also brings some …

Ensemble learning is an effective method to enhance the recognition accuracy of facial
expressions. The performance of ensemble learning can be affected by many factors, such …

In instance-based learning algorithms, the need to store a large number of examples as the
training set results in several drawbacks related to large memory requirements …

Ensemble pruning can improve the performance and reduce the storage requirements of an
integration system. Most ensemble pruning approaches remove low-quality or redundant …