ABSTRACT

Fake information in social media frequently causes social issues. The amount of fake information is smaller than that of real information, this leads to class imbalance. Some improved classification methods and metrics to resolve the imbalance and evaluate model performance have been proposed, respectively. However, the existing metrics for classification methods have many limitations. This paper proposes the robust metric, L-measure, that can reasonably evaluate all models with binary class imbalance with different IRs. L-measure also require less computation than the Matthews correlation coefficient. Finally, this paper demonstrates the validity of the proposed metric under different IRs with examples from UCI and Kaggle.

摘要

社交媒体中的虚假信息经常引发社会问题。虚假信息量小于真实信息量，这导致类不平衡。已经分别提出了一些改进的分类方法和度量来解决不平衡和评估模型性能。然而，现有的分类方法指标有很多局限性。本文提出了鲁棒性度量 L-measure，它可以合理地评估所有具有不同 IR 的二元类不平衡模型。L-measure 还需要比 Matthews 相关系数更少的计算。最后，本文通过来自 UCI 和 Kaggle 的示例证明了所提出的度量在不同 IR 下的有效性。