Hierarchical Text Categorization Based on Multiple Feature Selection and Fusion of Multiple Classifiers Approaches

Abstract

Hierarchical text categorization refers to assigning of one or more suitable category from a hierarchical category space to a document. In this paper, we used hierarchical feature selection method and multiple classifiers for the Hierarchical text categorization task. Experiments showed that the methods we used was effective, compared with flat classification, top-down level-based approach with the multiple feature selection method, the single classifier obtained better performance; reliability function was introduction to evaluate the determine by single classifier reliability, if the reliability function got a small value, multiple classifiers were used to give the determine which category the unlabeled document belong to, compared to single classifier, Multiple classifiers achieved better performance on flat and hierarchical corpuses, and the time cost increasing is little than using single main classifier.