关键词: 符号数据, 分类, 空间相关性分析, 支持向量机（SVM）, K-最近邻（KNN）

Abstract: Aiming at the problem of low classification accuracy for categorical data, this paper proposes a classification approach based on space correlation analysis by mining the space structure between attributes and labels. At first, the one-hot encoding is applied to expand dimensions for categorical data. Then a spatial representation method for categorical data based on mutual information and conditional entropy is defined. And support vector machine (SVM) and K-nearest neighbor (KNN) model are used respectively, and two classification algorithms are designed, namely SCA_SVM (SVM classification algorithm based on space correlation analysis) and SCA_KNN (KNN classification algorithm based on space correlation analysis). The approach can not only reflect the correlation between attributes and labels well, but also measure the distance or difference between different attributes effectively. The experiment results on the UCI datasets demonstrate that the proposed approach has a better classification performance.

Key words: categorical data, classification, space correlation analysis, support vector machine (SVM), K-nearest neighbor (KNN)