Ensemble learning: A survey

Omer Sagi, Lior Rokach

Research output: Contribution to journalReview articlepeer-review

674 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 languageEnglish
Article numbere1249
JournalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Volume8
Issue number4
DOIs
StatePublished - 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)

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