One Versus All Strategies of Multiclass SVM in Modeling Agarwood Oil Quality Classification
How to Cite?
Aqib Fawwaz Mohd Amidon, Noratikah Zawani Mahabob, Nurlaila Ismail, Zakiah Mohd Yusoff, Mohd Nasir Taib, "One Versus All Strategies of Multiclass SVM in Modeling Agarwood Oil Quality Classification," International Journal of Engineering Trends and Technology, vol. 69, no. 6, pp. 121-125, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I6P218
Agarwood oil is one of the most beneficial oil to the world community with a high demand. It is beneficial due to the variety of usages such as incense, traditional medicine, and perfumes. However, there has been a lack of research on the development of agarwood oil because there is no any standard grading model of agarwood oil was implemented. As a solution forms, it is very important to come out with a standard of quality classification model for agarwood oil grading’s. By continuing of the research for the development of this standard, specific algorithm function has been used to make sure the ability of this model is totally not in doubt. Support vector machine (SVM) has been chosen as a main model and for the specific function algorithm that has been chosen was multiclass function. Then, in the function, the one versus all (OVA) strategies has been used to make multiclass work and can be applied on SVM. The analysis work has involving the data taken from the previous researcher that consists of four classes of agarwood oil quality’s samples which are low, medium low, medium high and high quality. So, the output was the classification of quality between low, medium low, medium high or high quality while the input was the abundances (%) of compounds. The desk research has been conducted by using MATLAB software version r2020a for the simulation platform. The result showed that the model by using multiclass function has pass the performance criteria standard. The verdict in this research for sure will be valuable for the future research works of agarwood oil areas, especially quality classification part.
Agarwood oil, Mutliclass, One Versus All, Support Vector Machine, Performance criteria.
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