Ensemble learning: A survey

Omer Sagi; Lior Rokach

doi:10.1002/widm.1249

Ensemble learning: A survey

Omer Sagi
, Lior Rokach

Research output: Contribution to journal › Review article › peer-review

2765 Scopus citations

Abstract

Ensemble methods are considered the state-of-the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state-of-the-art ensemble methods and discusses current challenges and trends in the field. This article is categorized under: Algorithmic Development > Model Combining Technologies > Machine Learning Technologies > Classification.

Original language	English
Article number	e1249
Journal	Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Volume	8
Issue number	4
DOIs	https://doi.org/10.1002/widm.1249
State	Published - 1 Jul 2018

Keywords

boosting
classifier combination
ensemble models
machine-learning
mixtures of experts
multiple classifier system
random forest

ASJC Scopus subject areas

General Computer Science

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10.1002/widm.1249

Cite this

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title = "Ensemble learning: A survey",

abstract = "Ensemble methods are considered the state-of-the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state-of-the-art ensemble methods and discusses current challenges and trends in the field. This article is categorized under: Algorithmic Development > Model Combining Technologies > Machine Learning Technologies > Classification.",

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AB - Ensemble methods are considered the state-of-the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state-of-the-art ensemble methods and discusses current challenges and trends in the field. This article is categorized under: Algorithmic Development > Model Combining Technologies > Machine Learning Technologies > Classification.

KW - boosting

KW - classifier combination

KW - ensemble models

KW - machine-learning

KW - mixtures of experts

KW - multiple classifier system

KW - random forest

UR - https://www.scopus.com/pages/publications/85048720921

U2 - 10.1002/widm.1249

DO - 10.1002/widm.1249

M3 - Review article

AN - SCOPUS:85048720921

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ER -

Ensemble learning: A survey

Abstract

Keywords

ASJC Scopus subject areas

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