共查询到17条相似文献，搜索用时 125 毫秒
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研究医院综合科技评价中指标权重确定方法,比较主观赋权法与客观赋权法对评价结果的影响。基于中国医院科技量值指标体系,选取2018年度中国医院综合科技量值位居前100的医院各指标数据,计算熵值法、CRITIC法、独立性法、信息量法四种客观赋权法权重,通过加权计算得到四种方法100家医院的评价结果,与中国医院科技量值基于层次分析法确定权重的评价结果比较。100家医院研究数据信度质量高,信度系数值为0.883。熵值法权重与CRITIC、独立性、信息量法权重之间的相关关系系数值呈现出显著性。层次分析法与熵值法、CRITIC法、独立性法、信息量法五种方法计算的评价结果之间的相关系数值分别是0.920、0.984、0.992、0.976,p<0.01,有正相关关系。采用的四种客观赋权方法权重相关关系及评价结果相关关系全部呈现出显著性正相关。本例层次分析法与客观赋权方法相互得到验证,但这并不能代表客观赋权法适用于各类评价。实际工作中仍需要根据评价目的、管理需求及评价数据实际,系统比较研究选取合适的赋权方法。 相似文献
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探讨医院科技评价中评价指标权重的确定方法,比较主客观赋权法对评价结果的影响。基于以层次分析法确定指标权重的中国医院科技量值指标体系,选取《2018年度中国医院综合科技量值报告》中综合科技量值前100位医院的指标数据,分别采用熵值法、CRITIC法、独立性法、信息量法计算指标权重,通过加权计算得到这4种方法的评价结果,与采用层次分析法确定权重的中国医院科技量值结果进行比较。结果表明:(1)100家医院研究指标的信度系数值为0.883,数据信度质量高;(2)4种客观赋权法权重之间的相关系数值呈现出显著性;(3)采用层次分析法与4种客观赋权法计算得到的科技量值之间的相关系数值分别是0.920、0.984、0.992、0.976,全部呈现出正相关关系。研究验证了中国医院科技量值的主、客观评价结果,并提出客观赋权评价方法的选取要注意根据评价目的、管理需求及评价数据实际进行系统比较后选取。 相似文献
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客观赋权法在我国专利技术评价中的应用 总被引：1，自引：0，他引：1
指出了专利技术评价的重要性，并比较综合评价了主观赋权法和客观赋权法的特点，给出客观赋权法在我国专利技术评价中的应用，实例分析的结果令人满意。 相似文献
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利用客观赋权的熵权法和主观赋权法的G1法对指标进行组合赋权,建立了基于组合赋权法的科学技术综合评价模型,并对中国2000-2005年期间的科技发展进行了评价。本文的特色与创新一是构建了包含治理工业污染投资等反映科学发展观的评价指标体系。二是在G1法确定指标权重时,对准则层和各指标层分别用G1法赋权,解决G1法直接对指标层赋权、无法对准则层进行评价与排序的问题。三是对主观赋权G1法与客观赋权熵权法得到的权重组合赋权。这就避免了现有研究的客观赋权法无法反映专家经验、或主观赋权法无法反映客观条件变化的双重弊端。 相似文献
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客观赋权法本质及在科技评价中的应用研究——以学术期刊为例 总被引：1，自引：0，他引：1
客观赋权法主要有熵权法、主成分分析、因子分析、离散系数法、复相关系数法、CRITIC等,它们在科技评价中得到了广泛的应用。由于不同评价方法影响评价结果,因此对客观赋权法的本质进行分析具有重要意义。文章在对不同客观赋权法的本质进行分析的基础上,基于JCR2017经济学期刊进行了实证,并比较了不同客观赋权法的评价结果。研究表明:不同客观赋权法权重与评价结果相差很大;客观赋权评价方法的选取要慎重;主成分分析、因子分析因存在信息损失要慎重选用;复相关系数、CRITIC评价结果与其他评价方法相关度较低,要慎重选取;熵权法、离散系数法可用于同类指标评价。 相似文献
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银行系统风险评估方法研究 总被引：7，自引：0，他引：7
考虑银行系统风险评估的复杂性，以模糊综合评价法为基本出发点，通过选择几个主观权和若干个客观权得到一个最优组合赋权模型，同时提出了最优组合赋权的模糊综合评价模型。在银行系统风险的评价中，利用该模型既可以考虑政策、经验等主观因素的影响，又可以充分利用客观数据所带来的信息，使评价结果既符合客观实际又符合主观要求，从而达到有效地防范和控制金融风险的目的。 相似文献
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分析了影响PIS（Personalized Information Service）的影响因素,给出了PIS能力的评价指标体系,由于评价指标存在一定的模糊性和不确定性,提出了一种融合专家主观和客观赋权的专家聚类综合赋权法;利用该方法借助Matlab工具对PIS能力评价的各级指标进行赋权;最后对各指标的权值进行合理性的分析。 相似文献
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通过R聚类剔除反映信息重复的指标,利用因子分析遴选对科技评价影响较大的指标,构建了能够反映科学发展观内涵的科技评价指标体系。在此基础上,利用变异系数对主观G1权重、客观余弦夹角权重进行组合加权,建立了基于变异系数加权的组合赋权模型,并对中国14个典型的省份科技评价数据进行了实证。创新与特色:一是通过变异系数对主观G1权重、客观余弦夹角权重进行加权,构建了变异系数加权后组合权重最大的目标函数,进而确定了主、客观权重的组合系数θ_1、θ_2,反映了组合赋权中主、客观权重分布变异性越大、权重越大的赋权思路。弥补了现有组合赋权中主、客观权系数相等,无法有效反映专家知识经验和客观数据差异的不足。二是实证研究表明:科技投入不足是制约江西、广西两省科技发展较差的主要因素;科技对经济与社会的影响偏弱是制约四川省科技发展的关键因素;科技产出不足是制约黑龙江、山东、河南三省科技发展的瓶颈因素。 相似文献
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Ximing Li Ang Zhang Changchun Li Jihong Ouyang Yi Cai 《Information processing & management》2018,54(6):1345-1358
Topic models often produce unexplainable topics that are filled with noisy words. The reason is that words in topic modeling have equal weights. High frequency words dominate the top topic word lists, but most of them are meaningless words, e.g., domain-specific stopwords. To address this issue, in this paper we aim to investigate how to weight words, and then develop a straightforward but effective term weighting scheme, namely entropy weighting (EW). The proposed EW scheme is based on conditional entropy measured by word co-occurrences. Compared with existing term weighting schemes, the highlight of EW is that it can automatically reward informative words. For more robust word weight, we further suggest a combination form of EW (CEW) with two existing weighting schemes. Basically, our CEW assigns meaningless words lower weights and informative words higher weights, leading to more coherent topics during topic modeling inference. We apply CEW to Dirichlet multinomial mixture and latent Dirichlet allocation, and evaluate it by topic quality, document clustering and classification tasks on 8 real world data sets. Experimental results show that weighting words can effectively improve the topic modeling performance over both short texts and normal long texts. More importantly, the proposed CEW significantly outperforms the existing term weighting schemes, since it further considers which words are informative. 相似文献
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Term weighting for document ranking and retrieval has been an important research topic in information retrieval for decades. We propose a novel term weighting method based on a hypothesis that a term’s role in accumulated retrieval sessions in the past affects its general importance regardless. It utilizes availability of past retrieval results consisting of the queries that contain a particular term, retrieved documents, and their relevance judgments. A term’s evidential weight, as we propose in this paper, depends on the degree to which the mean frequency values for the relevant and non-relevant document distributions in the past are different. More precisely, it takes into account the rankings and similarity values of the relevant and non-relevant documents. Our experimental result using standard test collections shows that the proposed term weighting scheme improves conventional TF*IDF and language model based schemes. It indicates that evidential term weights bring in a new aspect of term importance and complement the collection statistics based on TF*IDF. We also show how the proposed term weighting scheme based on the notion of evidential weights are related to the well-known weighting schemes based on language modeling and probabilistic models. 相似文献
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The most popular world university rankings are routinely taken at face value by media and social actors. While these rankings are politically influential, they are sensitive to both the conceptual framework (the set of indicators) and the modelling choices made in their construction (e.g., weighting or type of aggregation). A robustness analysis, based on a multi-modelling approach, aims to test the validity of the inference about the rankings produced in the Academic Ranking of World Universities (ARWU) of Shanghai Jiao Tong University and those produced by the UK's Times Higher Education Supplement (THES). Conclusions are drawn on the reliability of individual university ranks and on relative country or macro regional performance (e.g., Europe versus USA versus China) in terms of the number of top performing institutions. We find that while university and country level statistical inferences are unsound, the inference on macro regions is more robust. The paper also aims to propose an alternative ranking which is more dependant on the framework than on the methodological choices. 相似文献
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The work described here initially formed part of a triangulation exercise to establish the effectiveness of the Query Term Order algorithm. It subsequently proved to be a reliable indicator for summarising English web documents. We utilised the human summaries from the Document Understanding Conference data, and generated queries automatically for testing the QTO algorithm. Six sentence weighting schemes that made use of Query Term Frequency and QTO were constructed to produce system summaries, and this paper explains the process of combining and balancing the weighting components. The summaries produced were evaluated by the ROUGE-1 metric, and the results showed that using QTO in a weighting combination resulted in the best performance. We also found that using a combination of more weighting components always produced improved performance compared to any single weighting component. 相似文献
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金融机构信用评价是现代金融管理的基础,有助于监管机构依据监管目标对银行类金融机构进行风险高低排序,进而实施差别化监管,有效防范金融风险。构建包含资本充足率、杠杆率、不良贷款率、不良资产率、逾期贷款比例、拨备覆盖率、贷款拨备率、授信集中度等指标的银行类金融机构信用综合评价指标体系。利用Theil系数对主观权重和客观权重组合确定指标最优权重,建立基于组合赋权方法的评价模型,对银行类金融机构信用状况进行评价。 相似文献
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[目的/意义] 伴随着网络化的科学交流,新的计量指标的提出,科研院校评价方法也随之改变。本文旨在探讨Altmetrics视角下科研院校学术影响力综合评价的可行途径,并以医学信息学领域为例进行实证研究,进而为院校影响力的全面综合判定提供参考。[方法/过程] 融合Altmetric.com和Web of Science两类数据源,编程并筛选获取627个院校的Altmetrics数据和引文数据,选取7个计量指标进行相关性分析。再使用熵权法和层次分析法给指标赋权,并在此基础上,用TOPSIS方法分别计算出主观和客观的院校得分,并与QS排名作对比。[结果/结论] 指标之间存在中强相关性,主观和客观赋权方法所得的院校综合排名QS医学与生命科学排名,三者具有较强一致性,进而验证了Altmetrics指标在院校影响力评价中的有效性。又分别把层次分析-TOPSIS和熵权-TOPSIS与QS进行了相关性检验,就整体角度来讲,熵权-TOPSIS的评价结果更好。此外,根据两个评价方法和QS前30院校之间的交集,筛选出了各方面综合表现俱佳的10所院校,美国院校占绝大多数。且根据与QS交集个数,在高水平院校中,层次分析-TOPSIS评价效果更好。 相似文献
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K.Sparck Jones 《Information processing & management》1979,15(3):133-144
Following successful initial tests of theoretically-based schemes for relevance weighting of search terms, further experiments were undertaken to validate these results. The experiments were designed to investigate weighting for a large document set, poor matching conditions, heterogeneous data, and limited relevance information, i.e. the use of weighting in more realistic conditions than the initial ones. The results confirm the earlier ones: very striking improvements in retrieval performance were obtained, especially for the theoretically best-founded weighting formula. The experiments illustrate a much more promising application of statistical methods to indexing and searching than any studied hitherto. 相似文献