Belief Entropy Tree and Random Forest: Learning from Data with Continuous Attributes and Evidential Labels

Entropy

卷:24

Kangkai Gao¹  Yong Wang¹  Liyao Ma² 

[1] Department of Automation, University of Science and Technology of China, Hefei 230027, China;

[2] School of Electrical Engineering, University of Jinan, Jinan 250022, China;

关键词: decision trees;    uncertain data;    belief entropy;    belief function;    random forest;    evidential likelihood;   

DOI  :  10.3390/e24050605

来源: DOAJ

【 摘 要 】

As well-known machine learning methods, decision trees are widely applied in classification and recognition areas. In this paper, with the uncertainty of labels handled by belief functions, a new decision tree method based on belief entropy is proposed and then extended to random forest. With the Gaussian mixture model, this tree method is able to deal with continuous attribute values directly, without pretreatment of discretization. Specifically, the tree method adopts belief entropy, a kind of uncertainty measurement based on the basic belief assignment, as a new attribute selection tool. To improve the classification performance, we constructed a random forest based on the basic trees and discuss different prediction combination strategies. Some numerical experiments on UCI machine learning data set were conducted, which indicate the good classification accuracy of the proposed method in different situations, especially on data with huge uncertainty.

【 授权许可】

Unknown