Approbation of the data classification method based on the SVM algorithm and the k nearest neighbors algorithm
Liliya Anatolievna Demidova, Yu. S. Sokolova · IOP Conference Series Materials Science and Engineering · 2021
Abstract The aim of the work is to test the classification method based on the combined use of SVM andkNN classifiers. At the first stage of the classification method, on the basis of the initial training dataset U, SVM classifier is developed and the width of the Ω area is determined. This Ω area contains all objects incorrectly classified by SVM classifier, which form together with correctly classified objects that have fallen into the Ω area and the corresponding object class labels from the Ω area a new datasetG. At the second stage of the classification method, thekNN classifier developed on the basis of information about the objects of the setU\Gis applied to all objects of the datasetGfrom the Ω area. The values of the parameters of thekNN classifier are determined experimentally in such a way as to ensure the highest possible accuracy of object classification. Since the correctly classified objects can also enter the Ω area formed in the above way, the condition for the applicability of the proposed method is a general improvement in the classification quality. The presented results of experimental studies confirm the effectiveness of the proposed method in the problem of classifying complex multidimensional data.