基于半监督支持向量机的期刊收稿系统自动分类方法
|
摘要
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. However,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-supervised support vector machine is proposed. A vector space model based on TF/IDF feature weights is put forward to realize eigenvector 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 results show that the average F1 of the proposed automatic classification method based on the semi-supervised support vector machine 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.
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. However,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-supervised support vector machine is proposed. A vector space model based on TF/IDF feature weights is put forward to realize eigenvector 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 results show that the average F1 of the proposed automatic classification method based on the semi-supervised support vector machine 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.
引文
[1]程维红,任胜利,沈锡宾,等.中国科协科技期刊数字出版及传播力建设[J].中国科技期刊研究,2014,25(3):340-345.CHENG Weihong,REN Shengli,SHEN Xibin,et al. Digital publishing and communication construction for science and technology periodicals of China Association for Science and Technology[J]. Chinese journal of scientific and technical periodicals,2014,25(3):340-345.
[2]丁洁,耿倩.基于SOA和云技术的论文网络投稿系统初探[J].电子测试,2013(8):212-213.DING Jie,GENG Qian. SOA and cloud-based system of papers submission system[J]. Electronic test,2013(8):212-213.
[3]张付志,李勇.基于标签内特征词的OA期刊站点自动分类方法[J].小型微型计算机系统,2014,35(1):60-63.ZHANG Fuzhi,LI Yong. An automatic classification approach for open access journal websites based on terms in tags[J].Journal of Chinese computer systems,2014,35(1):60-63.
[4] CHENG L,LIN H,ZHOU F,et al. Enhancing the accuracy of knowledge discovery:a supervised learning method[J]. BMC bioinformatics,2014,15(S12):1-9.
[5] SUN S,HUSSAIN Z,SHAWE-TAYLOR J. Manifold-preserving graph reduction for sparse semi-supervised learning[J].Neurocomputing,2014,124:13-21.
[6]高雪霞,炎士涛.基于WordNet词义消歧的语义检索研究[J].湘潭大学自然科学学报,2017,39(2):118-121.GAO Xuexia,YAN Shitao. Research on semantic retrieval of word sense disambiguation based on WordNet[J]. Natural science journal of Xiangtan University,2017,39(2):118-121.
[7]廖胜平,徐玲,鄢萌.基于采样的半监督支持向量机软件缺陷预测方法[J].计算机工程与应用,2017,53(14):161-166.LIAO Shengping,XU Ling,YAN Meng. software defect prediction using semi-supervised support vector machine with sampling[J]. Computer engineering and applications,2017,53(14):161-166.
[8] TIAN Y,LUO J. A new branch-and-bound approach to semisupervised support vector machine[J]. Soft computing,2017,21(1):245-254.
[9] YIN C,FENG L,MA L. An improved Hoeffding-ID datastream classification algorithm[J]. Journal of supercomputing,2016,72(7):2670-2681.
[10] CAO J,HUANG W,ZHAO T,et al. An enhance excavation equipments classification algorithm based on acoustic spectrum dynamic feature[J]. Multidimensional systems&signal processing,2017,28(3):921-943.
[11] SHEN H,YAN Y,XU S,et al. Evaluation of semi-supervised learning method on action recognition[J]. Multimedia tools&applications,2015,74(2):523-542.
[12]如先姑力·阿布都热西提,贺一峰,亚森·艾则孜.基于文本分类的维吾尔文数字取证研[J].现代电子技术,2016,39(10):9-13.Ruxianguli Abudurexiti,HE Yifeng,Yasen Aizezi. Research on Uyghur forensics based on text categorization[J]. Modern electronics technique,2016,39(10):9-13.
[2]丁洁,耿倩.基于SOA和云技术的论文网络投稿系统初探[J].电子测试,2013(8):212-213.DING Jie,GENG Qian. SOA and cloud-based system of papers submission system[J]. Electronic test,2013(8):212-213.
[3]张付志,李勇.基于标签内特征词的OA期刊站点自动分类方法[J].小型微型计算机系统,2014,35(1):60-63.ZHANG Fuzhi,LI Yong. An automatic classification approach for open access journal websites based on terms in tags[J].Journal of Chinese computer systems,2014,35(1):60-63.
[4] CHENG L,LIN H,ZHOU F,et al. Enhancing the accuracy of knowledge discovery:a supervised learning method[J]. BMC bioinformatics,2014,15(S12):1-9.
[5] SUN S,HUSSAIN Z,SHAWE-TAYLOR J. Manifold-preserving graph reduction for sparse semi-supervised learning[J].Neurocomputing,2014,124:13-21.
[6]高雪霞,炎士涛.基于WordNet词义消歧的语义检索研究[J].湘潭大学自然科学学报,2017,39(2):118-121.GAO Xuexia,YAN Shitao. Research on semantic retrieval of word sense disambiguation based on WordNet[J]. Natural science journal of Xiangtan University,2017,39(2):118-121.
[7]廖胜平,徐玲,鄢萌.基于采样的半监督支持向量机软件缺陷预测方法[J].计算机工程与应用,2017,53(14):161-166.LIAO Shengping,XU Ling,YAN Meng. software defect prediction using semi-supervised support vector machine with sampling[J]. Computer engineering and applications,2017,53(14):161-166.
[8] TIAN Y,LUO J. A new branch-and-bound approach to semisupervised support vector machine[J]. Soft computing,2017,21(1):245-254.
[9] YIN C,FENG L,MA L. An improved Hoeffding-ID datastream classification algorithm[J]. Journal of supercomputing,2016,72(7):2670-2681.
[10] CAO J,HUANG W,ZHAO T,et al. An enhance excavation equipments classification algorithm based on acoustic spectrum dynamic feature[J]. Multidimensional systems&signal processing,2017,28(3):921-943.
[11] SHEN H,YAN Y,XU S,et al. Evaluation of semi-supervised learning method on action recognition[J]. Multimedia tools&applications,2015,74(2):523-542.
[12]如先姑力·阿布都热西提,贺一峰,亚森·艾则孜.基于文本分类的维吾尔文数字取证研[J].现代电子技术,2016,39(10):9-13.Ruxianguli Abudurexiti,HE Yifeng,Yasen Aizezi. Research on Uyghur forensics based on text categorization[J]. Modern electronics technique,2016,39(10):9-13.