Quadratic tuned kernel parameter in non-linear support vector machine (SVM) for agarwood oil compounds quality classification

• Main Authors: Muhamad Addin Akmal, Mohd Raif, Nurlaila, Ismail, Nor Azah, Mohd Ali, Mohd Hezri Fazalul, Rahiman, Saiful Nizam, Tajuddin, Mohd Nasir, Taib
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2019
• Subjects: Q Science (General); QD Chemistry; T Technology (General); TP Chemical technology
• Online Access: http://umpir.ump.edu.my/id/eprint/33514/1/Quadratic%20tuned%20kernel%20parameter%20in%20non-linear%20support%20vector%20machine%20%28svm%29.pdf; http://umpir.ump.edu.my/id/eprint/33514/; https://doi.org/10.11591/ijeecs.v17.i3.pp1371-1376; https://doi.org/10.11591/ijeecs.v17.i3.pp1371-1376
• Institution: Universiti Malaysia Pahang

This paper presents the analysis of agarwood oil compounds quality classification by tuning quadratic kernel parameter in Support Vector Machine (SVM). The experimental work involved of agarwood oil samples from low and high qualities. The input is abundances (%) of the agarwood oil compounds and the output is the quality of the oil either high or low. The input and output data were processed by following tasks; i) data processing which covers normalization, randomization and data splitting into two parts in which training and testing database (ratio of 80%:20%), and ii) data analysis which covers SVM development by tuning quadratic kernel parameter. The training dataset was used to be train the SVM model and the testing dataset was used to test the developed SVM model. All the analytical works are performed via MATLAB software version R2013a. The result showed that, quadratic tuned kernel parameter in SVM model was successful since it passed all the performance criteria’s in which accuracy, precision, confusion matrix, sensitivity and specificity. The finding obtained in this paper is vital to the agarwood oil and its research area especially to the agarwood oil compounds classification system.