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 …

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 …

Inspired by Darwin's theory of natural selection to explain the diversity and adaptability of
life, Burnet's clonal selection theory explains the diversity and learning properties of the …

This paper aims to develop a two-layer emergency logistics system with a single depot and
multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire …

A voting-averaged (VOA) method is presented to combine an ensemble for the regression
tasks. VOA can select ensemble components dynamically using the hidden selectivity …

Decreasing the individual error and increasing the diversity among classifiers are two crucial
factors for improving ensemble performances. Nevertheless, the “kappa-error” diagram …

Ensemble methods that train multiple learners and then combine them for use, with Boosting
and Bagging as representatives, are a kind of state-of-theart learning approach. It is well …

In recent years, several approaches have been proposed for sentiment based classification
of online text. Out of the different contemporary approaches, supervised machine learning …

In this paper, we apply an immune-inspired approach to design ensembles of
heterogeneous neural networks for classification problems. Our proposal, called Bayesian …

This paper presents a new method for constructing ensembles of classifiers based on
immune network theory, one of the most interesting paradigms within the field of artificial …