%A Li Jiao,Huang Yongwen,Luo Tingting,Zhao Ruixue,Xian Guojian %T Automatic Classification Method Based on Multi-factor Algorithm %0 Journal Article %D 2020 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2020.0238 %P 43-51 %V 4 %N 11 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4960.shtml} %8 2020-11-25 %X

[Objective] This paper develops an automatic method for classification indexing, aiming to better manage massive information resources and conduct knowledge discovery. [Methods] First, we analyzed the relationship between keywords (e.g., subject terms/concepts) and classification numbers. Then, we designed a multi-factor weighted algorithm. Finally, we proposed a scheme for automatic classification indexing. [Results] We examined our method with annotated corpora of authoritative domains and standard data sets. For literature with single subject classification number, the precision, recall and F values were 84.1%, 79.8%, and 81.9% respectively. For literature with two subject classification numbers, the precision, recall and F values were 83.4%, 78.8%, and 81.0%. [Limitations] The accuracy and completeness of our method relies on high-quality corpora, and the indexing of interdisciplinary literature needs to be improved. [Conclusions] The proposed method could effectively finish the classification tasks.