Radial Basis Function (RBF) tuned Kernel Parameter of Agarwood Oil Compound for Quality Classification using Support Vector Machine (SVM)

The quality grading of agarwood oil is vital issue among producers. This paper presents the implementation of Radial Basis Function (RBF) tuned parameter in Support Vector Machine (SVM) for agarwood oil quality classification. The work involved of GC-MS based data of agarwood oil, were fed into SVM programming as input and the quality of oil as output. The high and low qualities of agarwood oil were pre-processed using MATLAB software version 2015a which involves of normalization, randomization and data division into training datasets (80%) and testing datasets (20%). By using ‘svmclassify’ script function in MATLAB version R2015a, the data is trained and tested as well as their performances were measured. Several criteria were chosen; specification, precision, accuracy, sensitivity, error rates, error test and mean square error in grading the agarwood oil. It can be concluded that the SVM modelwith RBF tuning was a success and passed all the criteria in classifying the agarwood oil qualities. The significant in this research is the reliable of the SVM handle with RBF as kernel parameter and its finding that contributed to the agarwood oil research area especially in grading system.

[1]  Saiful Nizam Tajuddin,et al.  Agarwood Essential Oil: Study on Optimum Parameter and Chemical Compounds of Hydrodistillation Extraction , 2015 .

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[4]  William Pao,et al.  Dynamic Well Bottom-Hole Flowing Pressure Prediction Based on Radial Basis Neural Network , 2014 .

[5]  Sanjeevikumar Padmanaban,et al.  Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System , 2017 .

[6]  J. R. Parker,et al.  Rank and response combination from confusion matrix data , 2001, Inf. Fusion.

[7]  Nurlaila Ismail,et al.  ANN modelling of agarwood oil significant chemical compounds for quality discrimination / Nurlaila Ismail , 2014 .

[8]  Chen Hin Keong,et al.  Heart of the matter: Agarwood use and trade and CITES implementation for Aquilaria malaccensis , 2000 .

[9]  Xiang Zhang,et al.  Data pre-processing in liquid chromatography-mass spectrometry-based proteomics , 2005, Bioinform..

[10]  Federico Girosi,et al.  An improved training algorithm for support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[11]  Barbara Hammer,et al.  A Note on the Universal Approximation Capability of Support Vector Machines , 2003, Neural Processing Letters.

[12]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[13]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[14]  S. Sathiya Keerthi,et al.  Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms , 2002, IEEE Trans. Neural Networks.

[15]  Michel Verleysen,et al.  Approximation by radial basis function networks , 2003 .

[16]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[17]  Dimitri P. Solomatine,et al.  Model Induction with Support Vector Machines: Introduction and Applications , 2001 .

[18]  Efstratios N. Pistikopoulos,et al.  Big data approach to batch process monitoring: Simultaneous fault detection and diagnosis using nonlinear support vector machine-based feature selection , 2018, Comput. Chem. Eng..

[19]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[20]  Bernhard Schölkopf,et al.  Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..

[21]  Yo-Ping Huang,et al.  SVM-based Decision Tree for medical knowledge representation , 2016, 2016 International Conference on Fuzzy Theory and Its Applications (iFuzzy).