Affiliations: [a] School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China | [b] Department of Supply Chain Management, W. P. Carey School of Business, Arizona State University, P.O. Box 873406, Tempe, AZ 85287, USA | [c] College of Communications Engineering, PLA University of Science and Technology, Nanjing, Jiangsu 210007 - China | [d] School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China. coff128@nju.edu.cn
Correspondence:
[*]
Address for correspondence: School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China
Abstract: (Objective) In order to increase classification accuracy of tea-category identification (TCI) system, this paper proposed a novel approach. (Method) The proposed methods first extracted 64 color histogram to obtain color information, and 16 wavelet packet entropy to obtain the texture information. With the aim of reducing the 80 features, principal component analysis was harnessed. The reduced features were used as input to generalized eigenvalue proximal support vector machine (GEPSVM). Winner-takes-all (WTA) was used to handle the multiclass problem. Two kernels were tested, linear kernel and Radial basis function (RBF) kernel. Ten repetitions of 10-fold stratified cross validation technique were used to estimate the out-of-sample errors. We named our method as GEPSVM + RBF + WTA and GEPSVM + WTA. (Result) The results showed that PCA reduced the 80 features to merely five with explaining 99.90% of total variance. The recall rate of GEPSVM + RBF + WTA achieved the highest overall recall rate of 97.9%. (Conclusion) This was higher than the result of GEPSVM + WTA and other five state-of-the-art algorithms: back propagation neural network, RBF support vector machine, genetic neural-network, linear discriminant analysis, and fitness-scaling chaotic artificial bee colony artificial neural network.
