评价指标体系的建立是各种评价问题研究中的不可缺少的重要研究内容,如果最终建立的评价指标体系的信息量损失过大,则无论建立什么样的评价指标体系都没有意义。通过将主成分分析方法与信息熵方法相结合,本文建立了最终构建的评价指标体系相对于海选评价指标体系的信息贡献测算模型。本文的创新与特色一是通过使用主成分方法与信息熵方法度量指标体系的信息含量,构建了评价指标体系相对于海选指标体系的信息贡献测算模型,改变了现有研究中只考虑评价指标如何筛选而忽视筛选出的评价指标相对于海选指标的信息贡献,解决了评价指标体系相对于海选指标体系的信息贡献的测算问题。二是实例计算表明从海选指标中筛选出的评价指标保留了94.4%的海选指标信息。
Abstract
The establishment of evaluation index system in various evaluation researches is an important and indispensable research content. If the information content of the eventual evaluation index system has too much loss, any evaluation index system makes no sense. Combining principal component analysis method and information entropy method, this paper establishes the measurement model of the information contribution of eventual evaluation index system compared to the mass-election evaluation index system. In this paper,the main characteristics and innovations lie in the following aspects. First, it uses principal component analysis and information entropy to measure information contents of the index system, to establish the measuring model of information contributions of the evaluation index system compared to the mass-election index system, to change the existing studies which only consider the selection problem of the evaluation index and neglect the information contribution problem, and to solve the measuring problem of the information contributions. Second, the example shows that eventual evaluation index system retains 94.4% information content of mass-election evaluation index system.
关键词
评价指标体系 /
海选指标体系 /
信息贡献 /
主成分 /
熵
Key words
evaluation index system /
mass-selection index system /
information contribution /
principal component /
entropy
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基金
国家自然科学基金项目(71171031,2012.01-2015.12);教育部科学技术研究项目(2011-10,2011.07-2014.10);国家自然科学基金青年科学基金项目(71201018,2012.07-2016.07);中国银监会银行业信息科技风险管理项目(2012-4-005,2012.02-2013.10);大连银行小企业信用风险评级系统与贷款定价项目(2012-01,2012.01-2013.12)。
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