Ensemble learning: A survey

Omer Sagi; Lior Rokach

doi:10.1002/widm.1249

Ensemble learning: A survey

Omer Sagi, Lior Rokach

Department of Software and Information Systems Engineering

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

1064 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

Computer Science (all)

Access to Document

10.1002/widm.1249

Cite this

@article{62cc2e6248724ae08482b30f3453ebff,

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.",

keywords = "boosting, classifier combination, ensemble models, machine-learning, mixtures of experts, multiple classifier system, random forest",

author = "Omer Sagi and Lior Rokach",

note = "Funding Information: This research is sponsored in part by (1) the Natural Sciences and Engineering Research Council of Canada (NSER C), (2) the National Science Foundation (NSF) under grant CM S-9318163, and (3) the W right Laboratory, Aeronautical Systems Center, Air Force Materiel Command, USAF, and the Advanced Research Projects Agency (ARPA) under grant number F33615-93-1-1330. Publisher Copyright: {\textcopyright} 2018 Wiley Periodicals, Inc.",

year = "2018",

month = jul,

day = "1",

doi = "10.1002/widm.1249",

language = "English",

volume = "8",

journal = "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery",

issn = "1942-4787",

publisher = "John Wiley and Sons Inc.",

number = "4",

}

TY - JOUR

T1 - Ensemble learning

T2 - A survey

AU - Sagi, Omer

AU - Rokach, Lior

N1 - Funding Information: This research is sponsored in part by (1) the Natural Sciences and Engineering Research Council of Canada (NSER C), (2) the National Science Foundation (NSF) under grant CM S-9318163, and (3) the W right Laboratory, Aeronautical Systems Center, Air Force Materiel Command, USAF, and the Advanced Research Projects Agency (ARPA) under grant number F33615-93-1-1330. Publisher Copyright: © 2018 Wiley Periodicals, Inc.

PY - 2018/7/1

Y1 - 2018/7/1

N2 - 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.

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 - http://www.scopus.com/inward/record.url?scp=85048720921&partnerID=8YFLogxK

U2 - 10.1002/widm.1249

DO - 10.1002/widm.1249

M3 - Review article

AN - SCOPUS:85048720921

SN - 1942-4787

VL - 8

JO - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

JF - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

IS - 4

M1 - e1249

ER -

Ensemble learning: A survey

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this