Classifier for Chinese Traditional Medicine with High-Dimensional and Small Sample-Size Data

摘要

The identification of Chinese traditional medicine is a difficult subject in pharmacology. The development of chemical measurement and pattern recognition make the chemical pattern recognition possible. In this paper a new chemical pattern recognition method is proposed, in which a simple method named corresponding-peak distance calculating is used to compute the distance between samples for nearest neighbor (NN) classifier, and genetic algorithm is used to optimize the parameters of NN classifier. With the proposed method in this paper, experiments are carried out on chromatogram data of Panax. The results indicate that the method can identify the medicine material of different harvest time or habitats, furthermore, this method which combines the pattern matching, genetic algorithm and NN classifier is robust, accurate and easy to be implemented.