A comparison of random forest based algorithms: random credal random forest versus oblique random forest

被引：71

作者：

Mantas, Carlos J. ^{[1
]}

Castellano, Javier G. ^{[1
]}

Moral-Garcia, Serafin ^{[1
]}

Abellan, Joaquin ^{[1
]}

机构：

[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada, Spain

来源：

SOFT COMPUTING | 2019年 / 23卷 / 21期

关键词：

Classification; Ensemble schemes; Random forest; Imprecise probabilities; Credal sets; RIDGE-REGRESSION; IMPRECISE PROBABILITIES; UNCERTAINTY MEASURES; DECISION TREES; CLASS NOISE; ENSEMBLE; CLASSIFICATION; CLASSIFIERS; MODEL;

D O I：

10.1007/s00500-018-3628-5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Random forest (RF) is an ensemble learning method, and it is considered a reference due to its excellent performance. Several improvements in RF have been published. A kind of improvement for the RF algorithm is based on the use of multivariate decision trees with local optimization process (oblique RF). Another type of improvement is to provide additional diversity for the univariate decision trees by means of the use of imprecise probabilities (random credal random forest, RCRF). The aim of this work is to compare experimentally these improvements of the RF algorithm. It is shown that the improvement in RF with the use of additional diversity and imprecise probabilities achieves better results than the use of RF with multivariate decision trees.

引用

页码：10739 / 10754

页数：16

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