Research and development of neural network ensembles: a survey

被引：48
|
作者：
Li, Hui [1 ]
Wang, Xuesong [2 ]
Ding, Shifei [3 ,4 ]
机构：
[1] Jiangsu Normal Univ, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China
[2] China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 221116, Jiangsu, Peoples R China
[3] China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China
[4] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
来源：
ARTIFICIAL INTELLIGENCE REVIEW | 2018年 / 49卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Artificial neural networks (ANNs); Granular computing (GrC); Neural network ensemble (NNE); PARTICLE SWARM OPTIMIZATION; LEARNING ALGORITHM; FEATURE-SELECTION; HYBRID ENSEMBLE; CLASSIFICATION; CLASSIFIERS; RECOGNITION; PREDICTION; DIVERSITY; PERFORMANCE;
D O I：
10.1007/s10462-016-9535-1
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A Neural Network Ensemble (NNE) combines the outputs of several individually trained neural networks in order to improve generalization performance. This article summarizes different approaches on the development and the latest studies on NNE. The introduction of the basic principles of NNE is followed by detailed descriptions of individual neural network generation method, conclusion generation method and fusion based on granular computing and NNE. In addition, for each of these methods we provide a short taxonomy in terms of their relevant characteristics, and analyze several of NNE applications, classic algorithms and contributions on various fields.
引用
收藏
页码：455 / 479
页数：25
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