摘要
【目的】通过基于机器学习的模型计算国际期刊的预警值,预测国际期刊质量变化趋势,为提醒科研人员审慎选择成果发表平台,帮助有关决策部门审查期刊质量提供智能化手段。【方法】构建期刊影响力强度、期刊影响力时效性、期刊特性、作者来源等4个维度的预警期刊指标体系,采用Pearson相关系数与XGBoost特征重要值相结合的方法进行特征的筛选,并对筛选后的特征进行时序性特征拓展,考虑学科差异性,在以医学类、工程科技类期刊为代表的标注数据集上通过XGBoost、SVM、逻辑回归以及Stacking融合等模型实现国际期刊异常行为识别和比较,最后基于XGBoost信息增益得到特征重要性排序。【结果】在医学类、工程科技类期刊上三种样本方案的研究结果表明,特征筛选后虽然会提升模型泛化性,但会轻微降低预警性能;特征筛选并拓展后能够提高期刊预警模型精度;自引率和投稿命中率等指标对模型具有较大贡献。【局限】限于数据实际获取情况,涉及学科范围较小且训练数据偏少,未加入论文处理费相关的期刊特征。【结论】构建的国际期刊异常行为预警模型适用于多学科环境,可以辅助机构和专家进行更有针对性的预警决策,提供了一种新的期刊质量管理方法。
[Objective]This paper creates an early warning mechanism for international journals,aiming to predict their quality changes and help researchers choose better publishing platforms.[Methods]We constructed an earlywarning index system for scholarly journals with their impact strength,influencing timeline,characteristics,and author demographics.Then,we combined Pearson correlation coefficient and the important values of XGBoost to select features.Third,we analyzed the features with XGBoost,SVM,logistic regression,and Stacking fusion to identify the abnormal behaviors.Finally,we ranked these features with XGBoost information gain.[Results]We examined our method with three sample datasets from medical and scientific journals.The generalization of the model could be improved with feature screening,which could also slightly reduce the early warning performance.Feature screening and expansion could improve the accuracy of the early warning model.The self-citation and submission acceptance rates play significant roles for the model.[Limitations]Due to the actual acquisition of data,the range of disciplines involved is small and the training data is small,and journal features related to article processing charge are not included.[Conclusions]The proposed model could help institutions and researchers improve decision making on the quality of international journals.
作者
吴金红
穆克亮
Wu Jinhong;Mu Keliang(School of Management,Wuhan Textile University,Wuhan 430200,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2022年第2期385-395,共11页
Data Analysis and Knowledge Discovery
基金
2020年度湖北省社会科学基金前期资助项目(项目编号:20ZD053)的研究成果之一。
关键词
国际期刊预警
特征选择
指标体系
机器学习
Early Warning of International Journals
Feature Selection
Indicator System
Machine Learning
