The F-measure for Research Priority

被引:0
|
作者
Ronald Rousseau [1 ,2 ]
机构
[1] University of Antwerp, Faculty of Social Sciences
[2] Belgium & KU Leuven,Facultair Onderzoekscentrum
关键词
D O I
暂无
中图分类号
G353.1 [情报资料的分析和研究];
学科分类号
摘要
Purpose: In this contribution we continue our investigations related to the activity index(AI) and its formal analogs. We try to replace the AI by an indicator which is better suited for policy applications.Design/methodology/approach: We point out that fluctuations in the value of the AI for a given country and domain are never the result of that country's policy with respect to that domain alone because there are exogenous factors at play. For this reason we introduce the F-measure. This F-measure is nothing but the harmonic mean of the country's share in the world's publication output in the given domain and the given domain's share in the country's publication output.Findings: The F-measure does not suffer from the problems the AI does.Research limitations: The indicator is not yet fully tested in real cases.R&D policy management: In policy considerations, the AI should better be replaced by the F-measure as this measure can better show the results of science policy measures(which the AI cannot as it depends on exogenous factors).Originality/value: We provide an original solution for a problem that is not fully realized by policy makers.
引用
收藏
页码:1 / 18
页数:18
相关论文
共 50 条
  • [11] On the Bayes-optimality of F-measure maximizers
    Waegeman, Willem
    Dembczyński, Krzysztof
    Jachnik, Arkadiusz
    Cheng, Weiwei
    Hüllermeier, Eyke
    Journal of Machine Learning Research, 2015, 15 : 3333 - 3388
  • [12] On the Bayes-Optimality of F-Measure Maximizers
    Waegeman, Willem
    Dembczynski, Krzysztof
    Jachnik, Arkadiusz
    Cheng, Weiwei
    Huellermeier, Eyke
    JOURNAL OF MACHINE LEARNING RESEARCH, 2014, 15 : 3333 - 3388
  • [13] Experimental investigating the F-measure as similarity measure for automatic text summarization
    Alguliev, Rasim M.
    Aliguliyev, Ramiz M.
    APPLIED AND COMPUTATIONAL MATHEMATICS, 2007, 6 (02) : 278 - 287
  • [14] F-Measure as the Error Function to Train Neural Networks
    Pastor-Pellicer, Joan
    Zamora-Martinez, Francisco
    Espana-Boquera, Salvador
    Jose Castro-Bleda, Maria
    ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT I, 2013, 7902 : 376 - +
  • [15] An adaptation of a F-measure for automatic text summarization by extraction
    Mohamed Amine Boudia
    Reda Mohamed Hamou
    Abdelmalek Amine
    Ahmed Chaouki Lokbani
    Cluster Computing, 2020, 23 : 2389 - 2398
  • [16] An adaptation of a F-measure for automatic text summarization by extraction
    Boudia, Mohamed Amine
    Hamou, Reda Mohamed
    Amine, Abdelmalek
    Lokbani, Ahmed Chaouki
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2020, 23 (03): : 2389 - 2398
  • [17] Regularized F-Measure Maximization for Feature Selection and Classification
    Liu, Zhenqiu
    Tan, Ming
    Jiang, Feng
    JOURNAL OF BIOMEDICINE AND BIOTECHNOLOGY, 2009,
  • [18] Optimizing the Multiclass F-measure via Biconcave Programming
    Pan, Weiwei
    Narasimhan, Harikrishna
    Kar, Purushottam
    Protopapas, Pavlos
    Ramaswamy, Harish G.
    2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), 2016, : 1101 - 1106
  • [19] Implication Intensity: Randomized F-measure for Cluster Evaluation
    Li, Limin
    Wu, Junjie
    Zhu, Shiwei
    2009 6TH INTERNATIONAL CONFERENCE ON SERVICE SYSTEMS AND SERVICE MANAGEMENT, VOLS 1 AND 2, 2009, : 724 - 729
  • [20] Adjusted F-measure and kernel scaling for imbalanced data learning
    Maratea, Antonio
    Petrosino, Alfredo
    Manzo, Mario
    INFORMATION SCIENCES, 2014, 257 : 331 - 341