This paper presents a novel ensemble classifier generation technique RotBoost, which is
constructed by combining Rotation Forest and AdaBoost. The experiments conducted with …

So far, we've considered decision trees and random forest algorithms. We saw that random
forest is a bagging (bootstrap aggregating) algorithm—it combines the output of multiple …

Cluster ensembles generate a large number of different clustering solutions and combine
them into a more robust and accurate consensus clustering. On forming the ensembles, the …

In machine learning, two approaches outperform traditional algorithms: ensemble learning
and deep learning. The former refers to methods that integrate multiple base models in the …

Bagging, boosting, rotation forest and random subspace methods are well known re-
sampling ensemble methods that generate and combine a diversity of learners using the …

We present a new ensemble learning algorithm, DeepBoost, which can use as base
classifiers a hypothesis set containing deep decision trees, or members of other rich or …

Decision tree is a well-known Machine Learning algorithm that is used for regression and
classification. It is an inductive inference algorithm mainly known for its interpretability and …

One of the most popular machine learning algorithms is gradient boosting over decision
trees. This algorithm achieves high quality out of the box combined with comparably low …

In recent years, classifier ensemble techniques have drawn the attention of many
researchers in the machine learning research community. The ultimate goal of these …

Ensemble classification–combining the results of a set of base learners–has received much
attention in the machine learning community and has demonstrated promising capabilities in …