Abstract: Hierarchical classification is an important branch in the field of data mining,which organizes data into a hierarchical structure by mining information between data.However,inter-level error propagation is an inevitable problem in hierarchical classification.This paper proposes a hierarchical classification method based on multi-path selection of the association relationship between categories in the same level,which can effectively alleviate the problem of error propagation between levels.Firstly,the correlation matrix between categories is constructed by the distribution of predicted categories and true categories.Then,inspiring by the pointwise mutual information(PMI),it designes a measurement method RPMI of the degree of correlation between categories in the same level,and the degree of correlation between categories in the same level is calculated based on RPMI.Secondly,logistic regression is used recursively from top to bottom in the hierarchical structure to select prediction categories at each level,and multiple candidate categories at the current level are determined by selecting categories that are more closely related to the prediction categories.Finally,Random Forest is used to select the best prediction category from the results of multi-path prediction.The proposed method is evaluated on five datasets,demonstrating that the method has a good classification performance.

Key words: Hierarchical classification, Pointwise mutual information(PMI), Multipath selection, Statistics, Correlation degree