Ensemble Methods in Supervised Learning.

Lior Rokach

doi:10.1007/978-0-387-09823-4_50

Ensemble Methods in Supervised Learning.

Lior Rokach

Department of Software and Information Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. In this chapter we provide an overview of ensemble methods in classification tasks. We present all important types of ensemble methods including boosting and bagging. Combining methods and modeling issues such as ensemble diversity and ensemble size are discussed.

Original language	English
Title of host publication	Data Mining and Knowledge Discovery Handbook
Publisher	Springer, Boston, MA
Pages	959-979
Number of pages	21
ISBN (Electronic)	978-0-387-09823-4
ISBN (Print)	978-0-387-09822-7
DOIs	https://doi.org/10.1007/978-0-387-09823-4_50
State	Published - 2010

Access to Document

10.1007/978-0-387-09823-4_50

Cite this

@inbook{5d1d890998dc49d7b147f262acb547d9,

title = "Ensemble Methods in Supervised Learning.",

abstract = "The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. In this chapter we provide an overview of ensemble methods in classification tasks. We present all important types of ensemble methods including boosting and bagging. Combining methods and modeling issues such as ensemble diversity and ensemble size are discussed.",

author = "Lior Rokach",

note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.",

year = "2010",

doi = "10.1007/978-0-387-09823-4_50",

language = "English",

isbn = "978-0-387-09822-7",

pages = "959--979",

booktitle = "Data Mining and Knowledge Discovery Handbook",

publisher = "Springer, Boston, MA",

}

TY - CHAP

T1 - Ensemble Methods in Supervised Learning.

AU - Rokach, Lior

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2010

Y1 - 2010

N2 - The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. In this chapter we provide an overview of ensemble methods in classification tasks. We present all important types of ensemble methods including boosting and bagging. Combining methods and modeling issues such as ensemble diversity and ensemble size are discussed.

AB - The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. In this chapter we provide an overview of ensemble methods in classification tasks. We present all important types of ensemble methods including boosting and bagging. Combining methods and modeling issues such as ensemble diversity and ensemble size are discussed.

U2 - 10.1007/978-0-387-09823-4_50

DO - 10.1007/978-0-387-09823-4_50

M3 - Chapter

SN - 978-0-387-09822-7

SP - 959

EP - 979

BT - Data Mining and Knowledge Discovery Handbook

PB - Springer, Boston, MA

ER -

Ensemble Methods in Supervised Learning.

Abstract

Access to Document

Fingerprint

Cite this