The development of Earth observation (EO) technology has made the volume of remote
sensing data archiving continually larger, but the knowledge hidden in massive remote …

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 …

An ensemble of models is a set of learning models whose individual predictions are
combined in such a way that component models compensate for each other's weaknesses …

Ensemble selection deals with the reduction of an ensemble of predictive models in order to
improve its efficiency and predictive performance. The last 10 years a large number of very …

Random Forest is an ensemble supervised machine learning technique. Based on bagging
and random feature selection, number of decision trees (base classifiers) is generated and …

Artificial intelligence (AI) is a scientific discipline that aims to create intelligent machines.
Machine learning is a popular and practical AI subfield that aims to automatically improve …

1. Introduction to pattern classification. 1.1. Pattern classification. 1.2. Induction algorithms.
1.3. Rule induction. 1.4. Decision trees. 1.5. Bayesian methods. 1.6. Other induction …

Load forecasting implies directly in financial return and information for electrical systems
planning. A framework to build wavenet ensemble for short-term load forecasting is …

Diversity among individual classifiers is recognized to play a key role in ensemble, however,
few theoretical properties are known for classification. In this paper, by focusing on the …