Nonlinear Random Forest Classification, a Copula-Based Approach

Applied Sciences

Radko Mesiar¹  Ayyub Sheikhi² 

[1] Department of Mathematics and Descriptive Geometry, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Radlinskeho 11, 810 05 Bratislava, Slovakia;Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman 7616913439, Iran;

关键词: random forest;    copula;    mutual information;    classification;    COVID-19;   

DOI  :  10.3390/app11157140

来源: DOAJ

【 摘 要 】

In this work, we use a copula-based approach to select the most important features for a random forest classification. Based on associated copulas between these features, we carry out this feature selection. We then embed the selected features to a random forest algorithm to classify a label-valued outcome. Our algorithm enables us to select the most relevant features when the features are not necessarily connected by a linear function; also, we can stop the classification when we reach the desired level of accuracy. We apply this method on a simulation study as well as a real dataset of COVID-19 and for a diabetes dataset.

【 授权许可】

Unknown