Classifier ensembling research has been one of the most active areas of machine learning
for a long period of time. The main aim of generating combined classifier ensembles is to …

This paper introduces a novel approach for building heterogeneous ensembles based on
genetic programming (GP). Ensemble learning is a paradigm that aims at combining …

Classifier ensemble algorithms allow for the creation of combined machine learning models
that are more accurate and generalizable than individual classifiers. However, creating such …

Ensemble classifiers methods are considered as one of the strongest classification
approaches in different applications. A key factor in creating a successful ensemble is to …

In the classification task, the ensemble of classifiers have attracted more and more attention
in pattern recognition communities. Generally, ensemble methods have the potential to …

Over the years, research has advanced in the field of classifier ensembles. Several ways to
improve its efficiency have emerged, for homogeneous and heterogeneous ensembles. One …

Classification is one of the most studied tasks in data mining and machine learning areas
and many works in the literature have been presented to solve classification problems for …

One of crucial issue in the design of combinational classifier systems is to keep diversity in
the results of classifiers to reach the appropriate final result. It's obvious that the more …

When a multiple classifier system is employed, one of the most popular methods to
accomplish the classifier fusion is the simple majority voting. However, when the …

Clustering based ensemble of classifiers have shown a significant improvement in
classification accuracy in many real-world applications. Most of the existing clustering-based …