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
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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收藏
页数:18
相关论文
共 153 条
  • [1] Random Projection Random Discretization Ensembles-Ensembles of Linear Multivariate Decision Trees
    Ahmad, Amir
    Brown, Gavin
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2014, 26 (05) : 1225 - 1239
  • [2] Al Iqbal MR, 2012, LECT NOTES COMPUT SC, V7664, P599, DOI 10.1007/978-3-642-34481-7_73
  • [3] Ali K.M, 1995, On the link between error correlation and error reduction in decision tree ensembles
  • [4] Amit Y., 1994, DTIC DOCUMENT
  • [5] [Anonymous], ARXIV160305850
  • [6] [Anonymous], ROUGH SETS FUZZY SET
  • [7] [Anonymous], 2014, IEEE Sympos Comput Intell Ensemble Learn, DOI DOI 10.1109/CIEL.2014.7015739
  • [8] [Anonymous], J IMAGE VIDEO PROCES
  • [9] Avidan S, 2006, LECT NOTES COMPUT SC, V3954, P386
  • [10] On voting-based consensus of cluster ensembles
    Ayad, Hanan G.
    Kamel, Mohamed S.
    [J]. PATTERN RECOGNITION, 2010, 43 (05) : 1943 - 1953