An enhanced random forest with canonical partial least squares for classification

被引：5

作者：

Li, Chuan-Quan ^{[1
]}

Lin, You-Wu ^{[2
]}

Xu, Qing-Song ^{[1
]}

机构：

[1] Cent South Univ, Sch Math & Stat, Changsha 410083, Hunan, Peoples R China

[2] Guangxi Teachers Educ Univ, Sch Math & Stat, Nanning, Guangxi, Peoples R China

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2021年 / 50卷 / 18期

基金：

中国国家自然科学基金;

关键词：

Canonical partial least squares; random forest; classification; feature rotation; CLASSIFIERS; REGRESSION; ENSEMBLE;

D O I：

10.1080/03610926.2020.1716249

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Recently, several variants of random forest have been derived for the classification problems, among which the rotation forest is an important type to improve the model's accuracy. In this article, we proposed a simple and effective variation of rotation forest, which the canonical partial least squares algorithm is employed to rotate the variable space of tree and then all the trees are combined being a "forest." Results of an experiment on a sample of 20 benchmark datasets show our method has better prediction performance comparing with random forest and rotation forest.

引用

页码：4324 / 4334

页数：11

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