Ensemble learning: A survey

被引：1665

作者：

Sagi, Omer ^{[1
]}

Rokach, Lior ^{[1
]}

机构：

[1] Ben Gurion Univ Negev, Dept Software & Informat Syst Engn, Beer Sheva, Israel

来源：

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY | 2018年 / 8卷 / 04期

基金：

美国国家科学基金会; 加拿大自然科学与工程研究理事会;

关键词：

boosting; classifier combination; ensemble models; machine-learning; mixtures of experts; multiple classifier system; random forest; CLASSIFIER ENSEMBLES; ROTATION FOREST; NEURAL-NETWORKS; CONSENSUS; ALGORITHMS; MODEL; TREES;

D O I：

10.1002/widm.1249

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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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页数：18

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