基于指数分层结构算法的数据可信度评估模型设计

针对传统的数据可信度评估模型存在分类适应性较差的问题，设计一种基于指数分层结构算法的数据可信度评估模型。分析实际数据资产管理过程，建立数据可信度评估指标体系；按照数据类型和数据间存在的周期性关系补充待评估数据中的缺漏数据，完成对待估数据的预处理；将数据归一化后生成数据集合，并根据数据间的相关系数建立亚超度量空间，生成指数分层结构树，结合层次分析法完成对可信度模型的设计。实验结果表明，与传统评估模型相比，所提模型的分类适应性更强，数据查全率更高，应用优势更明显。

Design of Data Reliability Evaluation Model Based on Index Hierarchical Structure Algorithm

To solve the problem of poor classification adaptability in traditional data credibility evaluation models, a data credibility evaluation model based on exponential hierarchical structure algorithm is designed. This paper analyzes the actual data asset management process, establishes the data credibility evaluation index system; supplies the missing data in the data to be evaluated according to the data type and the periodical relationship between the data, and completes the pretreatment of the estimated data; generates the data set after data normalization, establishes the sub-hypermetric space according to the correlation coefficient between the data, and generates the exponential hierarchical structure tree. The reliability model is designed by combining the analytic hierarchy process. The experimental results show that compared with the traditional evaluation model, the proposed model has better classification adaptability, higher data recall rate and more obvious application advantages.