Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

The assessment of financial credit risk is an important and challenging research topic in the
area of accounting and finance. Numerous efforts have been devoted into this field since the …

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

Banks take great care when dealing with customer loans to avoid any improper decisions
that can lead to loss of opportunity or financial losses. Regarding this, researchers have …

摘要集成学习因其能显著提高一个学习系统的泛化能力而得到了机器学习界的广泛关注,
但随着基学习机数目的增多, 集成学习机的预测速度明显下降, 其所需的存储空间也迅速增加 …

Several pruning strategies that can be used to reduce the size and increase the accuracy of
bagging ensembles are analyzed. These heuristics select subsets of complementary …

The concept of a classifier competence is fundamental to multiple classifier systems (MCSs).
In this study, a method for calculating the classifier competence is developed using a …

For better service and energy savings, improved fault detection and diagnosis (FDD) of
building energy systems is of great importance. To achieve this aim, ensemble learning is …

This article presents a machine learning approach in a heterogeneous group of algorithms
in a transport type model for the optimal distribution of tasks in safety-critical systems (SCS) …

This note presents a chronological review of the literature on ensemble learning which has
accumulated over the past twenty years. The idea of ensemble learning is to employ multiple …