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A Novel Pareto-VIKOR Index for Ranking Scientists’ Publication Impacts: A Case Study on Evolutionary Computation Researchers

机译:排名科学家出版物影响力的新型帕累托-VIKOR指数:以进化计算研究人员为例

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Scientists' publication impacts ranking is an important topic in scientometrics which is performed based on various proposed criteria. One of the well-known indicators is h-index which evaluates researchers achievements based on number of citations. The h-index has utilized in many research data sources because of its appropriate properties, but similar to other assessment indicators, it has own disadvantages. hindex cannot give a fair comparison between junior and senior researches. There are two reasons for this unfair comparison: (1) h-index depends on the research period of scholars and (2) the number of received citations can be increased by time, even if researcher doesn't publish new papers, the h-index increases. Consequently, in addition to h-index, the number of the years of academic research (called the research period) is preferable to be considered as an independent indicator, which makes us able to have a more fair evaluation. So these two objectives, maximizing h-index and minimizing research period, can be considered as a multi-criteria comparison task to assess researchers. In this paper, we propose a strategy based on Pareto dominance ranking which uses dominance concept to obtain an order for researchers. In order to complete ranking between scientists in the same rank, a multi-criteria decision making measure called VIKOR is utilized. Therefore, a total ranking measure (P-V index) is obtained using Perto front concept and VIKOR measure. The proposed method is applied on 235 researchers who are conducting research on Evolutionary Computation (EC) topic. The h-index value and the research period of scholars are collected via Google Scholar service. P-V index obtains 26 Pareto ranks for all researchers and places six EC scientists on the first Pareto front.
机译:科学家的出版物影响排名是基于各种拟议标准进行的科学计量学中的重要课题。众所周知的指标之一是H-Index,其根据引文的数量评估研究人员的成就。 H-Index在许多研究数据来源中使用,因为其适当的属性,但类似于其他评估指标,它具有自身的缺点。 Hindex不能在初级和高级研究之间进行公平比较。这个不公平比较有两个原因:(1)H-Indeg取决于学者的研究期限和(2)所接受的引用人数可以增加时间,即使研究人员没有发布新文件,H-指数增加。因此,除了H-Index之外,学术研究的年数(称为研究期)是优选认为是独立指标,这使得能够更公平的评估。因此,这两个目标最大化了H-Index和最小化研究时期,可以被视为评估研究人员的多标准比较任务。在本文中,我们提出了一种基于帕累托优势排名的策略,该策略利用主导概念来获得研究人员的命令。为了在同一等级中的科学家之间完成排名,利用了一个名为Vikor的多标准决策措施。因此,使用Perto Front Concept概念和Vikor测量获得总排列量度(P-V索引)。该方法适用于235名研究人员,他们正在进行进化计算(EC)主题。通过Google学者服务收集H-Index值和学者研究期。 P-V索引获得所有研究人员的26个帕累托等级,并将六名欧共体科学家放在第一个帕累托前面。

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