Semantic Scholar Open Access 2022 893 sitasi

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

Ibomoiye Domor Mienye Yanxia Sun

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (2)

Ibomoiye Domor Mienye

Yanxia Sun

Format Sitasi

APA MLA BibTeX

Mienye, I.D., Sun, Y. (2022). A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects. https://doi.org/10.1109/ACCESS.2022.3207287

Akses Cepat

Lihat di Sumber doi.org/10.1109/ACCESS.2022.3207287

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Total Sitasi: 893×
Sumber Database: Semantic Scholar
DOI: 10.1109/ACCESS.2022.3207287
Akses: Open Access ✓