Statistical Learning BSVM Model to the Problem of Agarwood Oil Quality Categorization

Abstract

This paper presents an attemption to empirically assess statistical learning model Boolean Support Vector Machines (BSVM) to the problem of agarwood oil quality categorization. The modelling starts with data pre-processing of seven significant chemical compounds of agarwood oil, from high and low qualities. During this stage, the data was randomized, normalized and divided into training and testing parts. 80% of the training part was induced as examples and create the maximum margin hyperplane to separates high and low groups in a binary setting and build the model. Another 20% of testing part was used to validate the developed model. MATLAB software version R2016a was used to perform all the analysis. The result obtained a good model utilizing SVM in classifying agarwood oil significant volatile compound quality. The model achieved minimum of 80 % for precision, confusing matrix, accuracy, sensitivity and specificity. The finding in this study will benefit further work and application for agarwood oil research area especially its classification in quality of agarwood oil and many others.