重采样在微博机器人识别中的应用研究

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Abstract With the increasing number of microblog robot accounts, its identification has become a prominent problem in the current data mining field. To deal with the imbalance data issue in this task, we choose a large data set to explore the influence of resampling on the supervised learning algorithms and propose a novel microblog robot recognition framework combined with resampling. A variety of indexes have been used to evaluate the performance of 7 supervised learning algorithms on imbalanced validation sets based on 5 different resampling methods. The experimental results show that the Recall of the trained model from the small balanced training set will be seriously reduced in real situations, while the framework combined with resampling can significantly improve the recognition of robot accounts. The NearMiss undersampling method can increase the Recall, while the ADASYN oversampling method will improve the G_mean measure. Generally speaking, the release time, publishing region, and release interval are important features to distinguish normal users from robots. At the same time, resampling can adjust the rank of features that the machine learning algorithm depends on so that the model can get better performance.

Key words： microblog robots label imbalanced learning resampling NearMiss ADASYN

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	Articles by authors
	LUO Yunsong
	HUANG Muyu
	JIATao

Cite this article:

LUO Yunsong,HUANG Muyu,JIATao. The Application of Resampling in Recognition of Microblog Robots[J]. , 2021, 35(12): 133-148.

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http://jcip.cipsc.org.cn/EN/ OR http://jcip.cipsc.org.cn/EN/Y2021/V35/I12/133

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