Ensemble-based classifiers

被引：1782

作者：

Rokach, Lior ^{[1
]}

机构：

[1] Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel

来源：

ARTIFICIAL INTELLIGENCE REVIEW | 2010年 / 33卷 / 1-2期

关键词：

Ensemble of classifiers; Supervised learning; Classification; Boosting; RANDOM SUBSPACE METHOD; MULTIPLE CLASSIFIERS; NEURAL NETWORKS; DECISION TREES; CLASSIFICATION; DECOMPOSITION; ALGORITHM; DIVERSITY; EXPERTS; ACCURACY;

D O I：

10.1007/s10462-009-9124-7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. Researchers from various disciplines such as statistics and AI considered the use of ensemble methodology. This paper, review existing ensemble techniques and can be served as a tutorial for practitioners who are interested in building ensemble based systems.

引用

页码：1 / 39

页数：39

共 131 条

[11] Random forests [J].