Machine learning algorithms are increasingly used in various remote sensing applications
due to their ability to identify nonlinear correlations. Ensemble algorithms have been …

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 …

Ensemble learning techniques have achieved state-of-the-art performance in diverse
machine learning applications by combining the predictions from two or more base models …

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 …

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 …

Ensemble methods that train multiple learners and then combine them to use, with Boosting
and Bagging as representatives, are well-known machine learning approaches. It has …

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 …

Credit risk assessment is a crucial element in credit risk management. With the extensive
research on consumer credit risk assessment in recent decades, the abundance of literature …

Most machine learning tasks can be categorized into classification or regression problems.
Regression and classification models are normally used to extract useful geographic …

Ensemble learning, as a popular method to tackle concept drift in data stream, forms a
combination of base classifiers according to their global performances. However, concept …