基于半监督支持向量机的期刊收稿系统自动分类方法

An automatic classification method based on semi-supervised support vector machine for periodical manuscript acceptance system

现阶段的期刊收稿系统主要采用人工方式将投稿论文分配给相关专业领域的审稿专家,从而完成论文审稿。但是当面对大量的稿件时,人工分配方式存在效率较低,不能满足期刊时效性需求。针对以上问题,为了实现投稿论文的自动分配,建立一种基于半监督支持向量机的论文自动分类方法。首先提出了基于TF/IDF特征项权重的向量空间模型来实现论文的特征向量表示;然后采用半监督支持向量机对论文数据集进行分类;最后通过对某期刊收稿实例的分析,验证了该方法的有效性。实验结果表明,提出的基于半监督支持向量机的期刊收稿系统自动分类方法的平均F1的结果约为68%,从而在满足一定准确度的条件下提高了收稿系统的工作效率。

In the current periodical manuscript acceptance system,the manual mode is mainly adopted to distribute the submitted e-mail manuscripts to the review experts in relevant professional fields,so as to complete manuscript review.Howev-er,the manual distribution mode is less efficient when facing with a large quantity of manuscripts.In order to solve the above problems and realize automatic distribution of submitted manuscripts,an automatic classification method based on the semi-su-pervised support vector machine is proposed.A vector space model based on TF/IDF feature weights is put forward to realize ei-genvector representation of manuscripts.The semi-supervised support vector machine is used to classify datasets of manuscripts.The validity of the method was verified by analyzing manuscript acceptance instances of a certain journal.The experimental re-sults show that the average F1 of the proposed automatic classification method based on the semi-supervised support vector ma-chine for the periodical manuscript acceptance system is about 68%,which can improve the work efficiency of the periodical manuscript acceptance system while satisfying a certain accuracy condition.