In this paper,a functional data analysis method is used to select temperature data of 24 h per day as an independent curve sample.On this basis,a functional k-nearest neighbors(KNN)classifica-tion model is established to classify and discriminate average PM 2.5 concentration of the day.The quadratic kernel function,exponential kernel function,and triangle kernel function are selected to es-tablish the kNN classification model,and the results are analyzed.Through comparison,it is found that the kNN classification model using triangle kernel function is the most accurate and robust in classifying PM2.5 concentration.A comparative analysis is performed using the Nadaraya-Watson(NW)kernel method and the kNN classification model.The results show that the kNN classification model can effectively improve the classification accuracy.
functional data classificationk-nearest neighbors(KNN)kernel functionnonparametric statistics
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