Classification of agarwood oil quality using random forest and grid search crossvalidation / Mohamad Aqib Haqmi Abas ...[et al.]

This paper presents a machine learning technique
to classify the agarwood oil quality. The random forest classifier
model is used with the grid search cross validation technique to
classify the quality of agarwood oil. The data of agarwood oil
sample were obtained from Forest Research Institute Malaysia
(FRIM) and Universiti Malaysia Pahang, Malaysia. In this
experiment, the chemical compound abundances information of
the agarwood oil that has been extracted from GC-MS machine is
used as the input feature and the quality of the sample oil which
is high quality and low quality is used as the output feature.
Based on the result obtained from this study, using Gini impurity
measure as criterion combined with 3 level maximum depth of
decision trees and 3 number of maximum features for each tree
provides the best classification accuracy of the agarwood oil
quality sample at 100% and performance measure scores of 1.0.