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Indexed by:期刊论文

Date of Publication:2018-02-01

Journal:IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS

Included Journals:SCIE、EI

Volume:E101D

Issue:2

Page Number:504-511

ISSN No.:1745-1361

Key Words:protein-protein interaction; TrAdaBoost; actively transfer learning; relative distribution

Abstract:Protein-Protein Interaction Extraction (PPIE) from biomedical literatures is an important task in biomedical text mining and has achieved great success on public datasets. However, in real-world applications, the existing PPI extraction methods are limited to label effort. Therefore, transfer learning method is applied to reduce the cost of manual labeling. Current transfer learning methods suffer from negative transfer and lower performance. To tackle this problem, an improved TrAdaBoost algorithm is proposed, that is, relative distribution is introduced to initialize the weights of TrAdaBoost to overcome the negative transfer caused by domain differences. To make further improvement on the performance of transfer learning, an approach combining active learning with the improved TrAdaBoost is presented. The experimental results on publicly available PPI corpora show that our method outperforms TrAdaBoost and SVM when the labeled data is insufficient, and on document classification corpora, it also illustrates that the proposed approaches can achieve better performance than TrAdaBoost and TPTSVM in final, which verifies the effectiveness of our methods.