Analyzing Random Forest Classifier with Different Split Measures

被引：6

作者：

Kulkarni, Vrushali Y. ^{[1
,2
]}

Petare, Manisha ^{[2
]}

Sinha, P. K. ^{[3
,4
]}

机构：

[1] COEP, Pune, Maharashtra, India

[2] MIT, Pune, Maharashtra, India

[3] CDAC, HPC, Pune, Maharashtra, India

[4] CDAC, R&D, Pune, Maharashtra, India

来源：

PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON SOFT COMPUTING FOR PROBLEM SOLVING (SOCPROS 2012) | 2014年 / 236卷

关键词：

Classification; Split measures; Random forest; Decision tree;

D O I：

10.1007/978-81-322-1602-5_74

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 is an ensemble supervised machine learning technique. The principle of ensemble suggests that to yield better accuracy, the base classifiers in the ensemble should be diverse and accurate. Random forest uses decision tree as base classifier. In this paper, we have done theoretical and empirical comparison of different split measures for induction of decision tree in Random forest and tested if there is any effect on the accuracy of Random forest.

引用

页码：691 / 699

页数：9

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