Ensemble learning: A survey

被引：1881
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：
加拿大自然科学与工程研究理事会; 美国国家科学基金会;
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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