LF-LDA: A Topic Model for Multi-label Classification
计算机科学
人工智能
作者
Yongjun Zhang,Jialin Ma,Zijian Wang,Bolun Chen
出处
期刊:Lecture notes on data engineering and communications technologies日期:2017-05-28卷期号:: 618-628被引量:4
标识
DOI:10.1007/978-3-319-59463-7_62
摘要
The textual data grows explosively with the advent of the era of big data, a significant portion of textual data is text documents labeled with multi-label such as the papers with keywords. Multi-label classification is a power technology to handle the multi-labeled textual data, but a huge room stays for improving the effect of multi-label classifying for textual data. This paper introduces labeled LDA with function terms (LF-LDA), a topic model that extracts noisy function terms from textual data to improve the performance of multi-label classification. The experimental result on RCV1-v2 textual dataset shows that LF-LDA can outperform the other two state-of-art multi-label classifiers: Tuned SVM and L-LDA on both Macro-F1 and Micro-F1 metrics. The low variance also indicates LF-LDA is a robust classifier.