基于因子分析和诱导密度算子的学术期刊组合评价

Combination Evaluation of Academic Journal Based on Factor Analysis and Induced Density Operator

[目的/意义]针对组合评价法可以实现优势互补、减少单一评价法产生偏差的情况,提出一种新的基于因子分析和诱导密度算子的学术期刊组合评价模型。该模型的提出对学术期刊的评价研究具有十分重要的意义。[方法/过程]首先,利用因子分析法通过提取公共因子来消除指标间的相关共线性,并确定基于因子分析的综合评价值;然后,以信息熵为诱导分量,利用诱导密度算子对评价信息进行集结,并确定基于诱导密度算子的综合评价值;最后,基于最小偏差法建立组合评价模型,以此确定最终的组合评价值并排序。[结果/结论]选取信息与新闻出版学期刊为研究对象,实证结果表明,该方法不仅能利用因子分析法的优点,有效消除指标间的相关共线性,而且还能利用诱导密度算子的优点,充分考虑评价信息的分布情况,是一种科学合理的学术期刊评价方法。

[Purpose/Significance]Aiming at this situation that the combination evaluation method can achieve the complementary advantages and reduce the deviations from the single method,this paper proposes a new combination evaluation model of academic journal based on factor analysis and induced density operator.[Method/Process]Firstly,the factor analysis is used to eliminate the relevance among the indexes by extracting the common factors,and determine the comprehensive evaluation value based on factor analysis.Secondly,the induced density operator is used to aggregate the evaluation information after taking the information entropy as induced component,and determine the comprehensive evaluation value based on induced density operator.Finally,the combination evaluation model is constructed by the minimum deviation method to determine the final combination evaluation value.[Result/Conclusion]By taking information and press publication journals as the research object,the empirical results show that this method is scientific and reasonable as it not only can use the advantages of factor analysis to eliminate the relevance among the indexes,but also can use the advantages of induced density operator to consider the distribution of information.