TY - GEN
T1 - A two-level stacking model for detecting abnormal users in Wechat activities
AU - Ling, Jiayuan
AU - Li, Gangmin
N1 - Funding Information:
ACKNOWLEDGMENT This work is partially supported by the AI University Research Centre (AI-URC) through XJTLU Key Programme Special Fund (KSF-P-02) and KSF-A-17. And it is also partially supported by Suzhou Science and Technology Programme Key Industrial Technology Innovation programme with project code SYG201840. We appreciate their support and guidance.
Funding Information:
This work is partially supported by the AI University Research Centre (AI-URC) through XJTLU Key Programme Special Fund (KSF-P-02) and KSF-A-17. And it is also partially supported by Suzhou Science and Technology Programme Key Industrial Technology Innovation programme with project code SYG201840. We appreciate their support and guidance.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Machine learning algorithms are widely employed in plenty of classification or regression problems. While in real business world, it is confronted with huge and disorder data pattern. To recognize different kinds of users on the internet accurately and fast becomes a challenge. In a Wechat online bargain activity, the staff found that some strange users are highly like robots or malicious users. Thus we tried a two-level stacking model to detect them. This design got a good result of 0.98 accuracy after the training phase and an accuracy of 0.90 in a new term of the testing set. Moreover, this model is adaptable to linear and nonlinear datasets because of its diverse stacking of first-level classifiers. Therefore, this paper indicates a potential of the stacking classification model in big data times.
AB - Machine learning algorithms are widely employed in plenty of classification or regression problems. While in real business world, it is confronted with huge and disorder data pattern. To recognize different kinds of users on the internet accurately and fast becomes a challenge. In a Wechat online bargain activity, the staff found that some strange users are highly like robots or malicious users. Thus we tried a two-level stacking model to detect them. This design got a good result of 0.98 accuracy after the training phase and an accuracy of 0.90 in a new term of the testing set. Moreover, this model is adaptable to linear and nonlinear datasets because of its diverse stacking of first-level classifiers. Therefore, this paper indicates a potential of the stacking classification model in big data times.
KW - Stacking model
KW - Wechat bargain activity
KW - classification
KW - gray market
KW - robot user
KW - user behavior
UR - http://www.scopus.com/inward/record.url?scp=85085514563&partnerID=8YFLogxK
U2 - 10.1109/ITCA49981.2019.00057
DO - 10.1109/ITCA49981.2019.00057
M3 - Conference Proceeding
AN - SCOPUS:85085514563
T3 - Proceedings - 2019 International Conference on Information Technology and Computer Application, ITCA 2019
SP - 229
EP - 232
BT - Proceedings - 2019 International Conference on Information Technology and Computer Application, ITCA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Information Technology and Computer Application, ITCA 2019
Y2 - 20 December 2019 through 22 December 2019
ER -