Deducing Optimal Classification Algorithm for Heterogeneous Fabric

TL;DR

This paper evaluates multiple machine learning algorithms on synthetic data to identify the most suitable classifier for heterogeneous rock fabric, highlighting Random Forest as optimal.

Contribution

It introduces a systematic comparison of algorithms on synthetic heterogeneous fabric data to determine the best classifier, specifically endorsing Random Forest.

Findings

Random Forest outperformed other algorithms on the synthetic dataset.

The study provides guidance for selecting classifiers in heterogeneous fabric analysis.

Synthetic data experiments validated the effectiveness of the recommended algorithm.

Abstract

For defining the optimal machine learning algorithm, the decision was not easy for which we shall choose. To help future researchers, we describe in this paper the optimal among the best of the algorithms. We built a synthetic data set and performed the supervised machine learning runs for five different algorithms. For heterogeneous rock fabric, we identified Random Forest, among others, to be the appropriate algorithm.