Classification of traditional Chinese medicine by nearest-neighbour classifier and genetic algorithm

Abstract

The identification of traditional Chinese medicine is a difficult subject in pharmacology. The development of chemical measurement and pattern recognition make chemical pattern recognition possible. In this paper a new chemical pattern recognition method named NN2GA is proposed, in which a simple method named corresponding-peak distance calculation is used to compute the distance between samples for a nearest neighbor (NN) classifier, and a genetic algorithm is used to optimize the parameters of the NN classifier. A method named NN3GA, which is realized by adding a parameter to NN2GA, is proposed to improve the performance of the classifier. Experiments are carried out on chromatogram data of Panax, and comparisons are made between NNPR, NN2GA, and NN3GA classifiers. The results indicate that the method which combines NN with a genetic algorithm can identify medicine material having different harvest times or habitats. Furthermore, this method is robust, accurate and easy to implement.