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.
CITATION STYLE
Zhang, Y., Ma, J., Wang, Z., & Chen, B. (2018). LF-LDA: A topic model for multi-label classification. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 6, pp. 618–628). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-59463-7_62
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