Nonlinear Random Forest Classification, a Copula-Based Approach

Radko Mesiar,Ayyub Sheikhi

doi:10.3390/app11157140

Abstract

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.

Highlights

Dimension reduction is a major area of interest within the field of data mining and knowledge discovery, especially in high-dimensional analysis
A copula-based algorithm has been employed in a random forest classification
The idea of this paper may be extended in some manners. One may use this idea in a multi-class random forest classification

Summary

Introduction

Dimension reduction is a major area of interest within the field of data mining and knowledge discovery, especially in high-dimensional analysis. The issue of machine learning has received considerable attention; a number of researchers have sought to perform more accurate dimension reductions in this issue [1,2]. There are many areas of statistics and machine learning that benefit from feature selection techniques. From the statistics point of view, Han and Liu et al (2013) [3] and Basabi (2008) [4] have applied feature selection for multivariate time series. Debashis et al (2008) [5] have investigated feature selection and regression in high-dimensional problems

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