Enhancing text classification with the Universum

Chien-Liang Liu, Ching Hsien Lee

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

The Universum is a data set that shares the same domain as the target problem, but does not comprise any category of interest. Recently, the concept of inference through contradictions has shown that the Universum provides a means for machine learning algorithms to encode prior knowledge into the model to improve performance. This work investigates whether text classification algorithms can benefit from the Universum when one has only a few labeled examples at hand. Additionally, this work proposes a confidence scheme to incorporate Universum into the learning process, and further devises a learning with Universum algorithm called Universum logistic regression (U-LR). The confidence scheme provides another means for machine learning algorithms to incorporate Universum into learning process. We conduct experiments on three data sets with several combinations. The experimental results indicate that the proposed method outperforms the other learning with Universum methods.

原文English
主出版物標題2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
編輯Jiayi Du, Chubo Liu, Kenli Li, Lipo Wang, Zhao Tong, Maozhen Li, Ning Xiong
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1147-1153
頁數7
ISBN(電子)9781509040933
DOIs
出版狀態Published - 19 10月 2016
事件12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016 - Changsha, China
持續時間: 13 8月 201615 8月 2016

出版系列

名字2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016

Conference

Conference12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
國家/地區China
城市Changsha
期間13/08/1615/08/16

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