Cross-domain text classification algorithm based on instance-transfer learning

Ruijun Liu; Jun Wang; Zhuo Yu; Yuqian Shi; Lun Zhang; Changjiang Ji; Xin Jin

doi:10.1117/12.2576021

12 October 2020 Cross-domain text classification algorithm based on instance-transfer learning

Ruijun Liu, Jun Wang, Zhuo Yu, Yuqian Shi, Lun Zhang, Changjiang Ji, Xin Jin

Proceedings Volume 11574, International Symposium on Artificial Intelligence and Robotics 2020; 1157406 (2020) https://doi.org/10.1117/12.2576021
Event: International Symposium on Artificial Intelligence and Robotics (ISAIR), 2020, Kitakyushu, Japan

Abstract

Cross-domain text classification has broad application prospects in the field of data mining. Since transfer learning can help target domain data to achieve the sharing and transfer of semantic information with the help of existing knowledge domains, transfer learning are generally used to achieve cross-domain text processing. Based on this, we propose a cross-domain text classification algorithm -MTrA. The algorithm is based on TrAdaBoost, taking into account the distribution differences between the source domain and the target domain. It uses the Maximum Mean Discrepancy(MMD) as the initial weight parameter of the two domain. MTrA adds a weight backfill factor that considers the accuracy of the source domain classification and balances the weight update method of the source domain data. Through the verification in the dataset 20 Newsgroups, Compared with the traditional TrAdaBoost algorithm, it improves the classification accuracy by 9.4% on average. it proves the effectiveness and advantages of the algorithm.

Citation Download Citation

Ruijun Liu, Jun Wang, Zhuo Yu, Yuqian Shi, Lun Zhang, Changjiang Ji, and Xin Jin "Cross-domain text classification algorithm based on instance-transfer learning", Proc. SPIE 11574, International Symposium on Artificial Intelligence and Robotics 2020, 1157406 (12 October 2020); https://doi.org/10.1117/12.2576021

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available