Journal
IEEE ACCESS
Volume 10, Issue -, Pages 99129-99149Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3207287
Keywords
Boosting; Classification algorithms; Prediction algorithms; Machine learning algorithms; Computational modeling; Bagging; Machine learning; Learning systems; Algorithms; classification; ensemble learning; fraud detection; machine learning; medical diagnosis
Categories
Funding
- South African National Research Foundation [120106, 132797]
- South African National Research Foundation Incentive [132159]
Ask authors/readers for more resources
Ensemble learning techniques have achieved state-of-the-art performance by combining predictions from multiple base models, with a focus on widely used algorithms such as random forest, AdaBoost, gradient boosting, XGBoost, LightGBM, and CatBoost. This overview aims to provide concise coverage of their mathematical and algorithmic representations, lacking in existing literature, for the benefit of machine learning researchers and practitioners.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available