A modified genetic algorithm optimized SVM for rapid classification of tea leaves using laser-induced breakdown spectroscopy
Mingyin Yao, Gangrong Fu, Tianbing Chen, Muhua Liu, Jiang Xu, Huamao Zhou, Xiuwen He, Lin Huang
Abstract
This work provides a modified adaptive mutation probability selection genetic algorithm to optimize the SVM model, which improved the accuracy of tea sample classification by LIBS and its recognition accuracy was higher than CV-SVM and PSO-SVM.
Topics & Concepts
Support vector machineLaser-induced breakdown spectroscopyGenetic algorithmPattern recognition (psychology)Selection (genetic algorithm)Artificial intelligenceComputer scienceAlgorithmBiological systemSpectroscopyMachine learningBiologyPhysicsQuantum mechanicsLaser-induced spectroscopy and plasmaSpectroscopy and Chemometric AnalysesIdentification and Quantification in Food