Because of its important evaluative function, journal impact factors began to be manipulated by anomalous self-citations. To deal with this scientific misconduct and its undesirable influences, in this paper, an automatic classification model for journals with anomalous self-citation was constructed based on previous research. First, a training journal set and three test journal sets of normal journals and abnormal journals were established and four features were selected from a feature set. Then, a classification model was learnt using the Deep Belief Network (DBN) method, which was successfully able to identify abnormal journals in the data sets. Third, Logistic Regression and Support Vector Machine were employed to learn the classification models, the classification performances for which were then compared with the DBN model. Finally, 1138 journals in twelve subject areas from the journal Citation Report (JCR) in 2014 were chosen as empirical journal samples for the DBN model, from which 6.9 percent of empirical journals were identified as suspect journals with anomalous self-citation.

中文翻译：

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更有效地识别异常自引期刊

由于其重要的评价功能，期刊影响因子开始被异常自引所操纵。针对这种科学不端行为及其不良影响，本文在前人研究的基础上，构建了异常自引期刊的自动分类模型。首先，建立一个训练期刊集和三个正常期刊和异常期刊的测试期刊集，从一个特征集中选取四个特征。然后，使用深度信念网络（DBN）方法学习了一个分类模型，该模型能够成功识别数据集中的异常期刊。第三，采用逻辑回归和支持向量机学习分类模型，然后将分类性能与DBN模型进行比较。最后，