A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

By Ibomoiye Domor Mienye, Yanxia Sun. IEEE Access, 2022

1,035 citations recorded by the source corpus.

Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering the three main ensemble methods: bagging, boosting, and stacking, their early development to the recent state-of-the-art algorithms. The study focuses on the widely used ensemble algorithms, including random forest, adaptive boosting (AdaBoost), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). An attempt is made to concisely cover their mathematical and algorithmic representations, which is lacking in the existing literature and would be beneficial to machine learning researchers and practitioners.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1023f93870ff700c2ce936348a250deae8bd11cf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mohammed Ali Moni
2. S. Uddin
3. Rohitash Chandra
4. Azal Ahmad Khan
5. Omkar Chaudhari
6. Farshid Hajati
7. Palak Mahajan

In Signal Processing

1. Rohitash Chandra
2. Azal Ahmad Khan
3. Omkar Chaudhari

Citations in the corpus

• Ensemble Learning for Disease Prediction: A Review (2023, background): “The boosting approach trains a weak learner, computes its predictions, selects training samples that were mistakenly categorised, and then trains a subsequent weak learner with an updated training set that includes the incorrectly classified instances from the previous training session [15].”
• A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, positive): “A survey on applications of ensemble learning methods for CI problems [35] suggested that random forest and XGBoost have been mostly used in the literature and both methods o ff er reliable performances.”