Title: Building SVM and PNN optimal classifiers based on GA-PLS algorithm and the application in infrared spectrum

Authors: Ai-Min Yang; Yang Han; Hong-Wei Xing; Yu-Zhu Zhang

Addresses: College of Science, North China University of Science and Technology, Tangshan 063000, China ' College of Science, North China University of Science and Technology, Tangshan 063000, China ' The Ministry of Education Key Laboratory with Modern Metallurgical Technology, North China University of Science and Technology, Tangshan 063000, China ' The Ministry of Education Key Laboratory with Modern Metallurgical Technology, North China University of Science and Technology, Tangshan 063000, China

Abstract: Olefin infrared spectrum is a comprehensive presentation of its feature data. If the structures are different, it will certainly lead to feature differences between the spectrums. In this paper, with olefin cis/trans IR spectrums in OMNIC IR database as research objects, we have designed four types of classifiers based on support vector machine (SVM) and probabilistic neural network (PNN) upon Fisher ratios and genetic algorithm and partial least square (GA-PLS), respectively, so as to select the optimal classifiers to apply into other databases. The results show that: all the optimal classifiers based on SVM and PNN are designed with GA-PLS algorithm; and when the corresponding feature sets include the 70 features and 50 features, respectively, selected by GA-PLS, the classifiers are optimal. Upon case verification, it is found that: SVM-GA-PLS classifier is more suitable for the prediction to olefin cis-structure and PNN-GA-PLS is more suitable for the prediction to olefin trans-structure.

Keywords: Fisher ratios; GAs; genetic algorithms; partial least squares; olefin cis-structure olefin trans-structure; SVM; support vector machines; PNN; probabilistic neural networks; olefin infrared spectrum.

DOI: 10.1504/IJAMC.2016.080977

International Journal of Advanced Media and Communication, 2016 Vol.6 No.2/3/4, pp.198 - 210

Available online: 10 Dec 2016 *

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