Application of SA-KNN combined with SVM based on different kernel functions in pattern knowledge mining
Chenzhi Wei, Peng Zhou, Dongdong Li, Yi Zhong, Yi Han · International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021) · 2022
Pattern knowledge plays an important role in text recognition. The classification and segmentation system under a large concept system needs to focus on the problem of pattern knowledge mining. The relationship between labels and concepts is divided into three types: equal, belong, and irrelevant. There is a need for a classification method to accurately classify the relationships between label and concept, and between concepts. In this paper, the classification algorithm proposed is SA-KNN-SVM, which can retain the advantages of fast K-nearest neighbor (KNN) model training time and good prediction effect while improving accuracy. The SA-KNN algorithm aims to continuously adjust the parameters and determine the number of the iterations through loop iterations, and then quickly find the accuracy rate corresponding to the different K values. SVM has many kinds of kernel functions, select the kernel function that makes the classification accuracy higher. And find the best parameters by looping an iterative grid search. The experimental results of the proposed algorithm show significant improvement in classification accuracy and processing time.