Evaluation of RBF and MLP in SVM kernel tuned parameters for agarwood oil quality classification

Agarwood oil, famously known as costly oil, extracted from the resinous of fragrant heartwood. The oil is getting high demand in the market especially from China, Vietnam, India, Middle East countries, and Japan because of its unique odour. As one of the researches in grading the quality of agarwood oil, the evaluation of kernel tuned parameter using Radial Basics Function (RBF) and Multilayer Perceptron (MLP) are presented in this paper to classify the quality of agarwood oil by using support vector machine (SVM). The work involved of selected agarwood oil sample from high to low quality. The output was agarwood oil quality either low or high and the input was the abundances (%) of agarwood oil compoundS. The input and output data were pre-processed by following works; data processing (normalisation, randomisation and data splitting into two parts in which training and testing dataset (ratio of 80%:20%) and data analysis using SVM modelling. The training dataset was used to train in developing the SVM model and the testing dataset was used to test/validate the developed SVM model. All the analytical works were performed automatically via MATLAB software version R2013a. The result showed that SVM model with RBF tuning is better than SVM model with MLP tuning and passed all the performance measures; accuracy, precision, confusion matrix, sensitivity and specificity. The finding in this study is significant and benefits further work and application for agarwood oil research area especially its classification.

[1]  Nor Azah Mohd Ali,et al.  Analysis Of High Quality Agarwood Oil Chemical Compounds By Means Of Spme/Gc-Ms And Z-Score Technique , 2013 .

[2]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[3]  Bidyut Baran Chaudhuri,et al.  Efficient training and improved performance of multilayer perceptron in pattern classification , 2000, Neurocomputing.

[4]  Regula Naef,et al.  The volatile and semi‐volatile constituents of agarwood, the infected heartwood of Aquilaria species: a review , 2011 .

[5]  Masakazu Ishihara,et al.  THREE SESQUITERPENES FROM AGARWOOD , 1991 .

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

[7]  Andrew D. Back,et al.  Radial Basis Functions , 2001 .

[8]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[9]  Shiou Yih Lee,et al.  Keeping Up Appearances: Agarwood Grades and Quality , 2016 .

[10]  Abdul Hamid Adom,et al.  Classification of Agarwood Oil Using an Electronic Nose , 2010, Sensors.

[11]  Leevan Ling,et al.  Method of approximate particular solutions for constant- and variable-order fractional diffusion models , 2015 .

[12]  Bengt Fornberg,et al.  Stable computations with flat radial basis functions using vector-valued rational approximations , 2016, J. Comput. Phys..

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

[14]  Salina Abdul Samad,et al.  Classification of beverages using electronic nose and machine vision systems , 2012, Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference.

[15]  Nurlaila Ismail,et al.  Agarwood Oil Quality Classifier Using Machine Learning , 2018 .

[16]  K. Samsudin,et al.  Evaluation of fall detection classification approaches , 2012, 2012 4th International Conference on Intelligent and Advanced Systems (ICIAS2012).

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

[18]  Michael C. Ferris,et al.  Interior-Point Methods for Massive Support Vector Machines , 2002, SIAM J. Optim..

[19]  Nurlaila Ismail,et al.  Analysis of algorithms variation in Multilayer Perceptron Neural Network for agarwood oil qualities classification , 2017, 2017 IEEE 8th Control and System Graduate Research Colloquium (ICSGRC).

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

[21]  Selangor Darul Ehsan,et al.  COMPARISON OF CHEMICAL PROFILES OF SELECTED GAHARU OILS FROM PENINSULAR MALAYSIA , 2008 .

[22]  J. Platt.  Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[23]  Yudong Zhang,et al.  A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy , 2016, Journal of Medical Systems.

[24]  Hind Taud,et al.  Multilayer Perceptron (MLP) , 2018 .

[25]  Rouslan A. Moro,et al.  Support Vector Machines (SVM) as a Technique for Solvency Analysis , 2008 .

[26]  Gareth A. Vio,et al.  Applications of radial basis functions to fluid-structure coupling , 2017 .

[27]  Michel Vacher,et al.  SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results , 2010, IEEE Transactions on Information Technology in Biomedicine.

[28]  Mohamad T. Musavi,et al.  On the training of radial basis function classifiers , 1992, Neural Networks.

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

[30]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .