A Study of Strength and Correlation in Random Forests

被引：0

作者：

Bernard, Simon ^{[1
]}

Heutte, Laurent ^{[1
]}

Adam, Sebastien ^{[1
]}

机构：

[1] Univ Rouen, LITIS EA 4108, F-76801 St Etienne, France

来源：

ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS | 2010年 / 93卷

关键词：

Random Forests; Ensemble of Classifiers; Strength; Correlation;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we present a study on the Random Forest (RF) family of classification methods, and more particularly on two important properties called strength and correlation. These two properties have been introduced by Brennan in the calculation of an upper bound of the generalization error. We thus propose to experimentally study the actual relation between these properties and the error rate in order to confirm and extend the Breiman theoretical results. We show that the error rate statistically decreases with the joint maximization of the strength and minimization of the correlation, and this for different sizes of RF.

引用

页码：186 / 191

页数：6