当前位置: X-MOL 学术IEEE Trans. Cybern. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Granular Data Aggregation: An Adaptive Principle of the Justifiable Granularity Approach
IEEE Transactions on Cybernetics ( IF 11.8 ) Pub Date : 2019-02-01 , DOI: 10.1109/tcyb.2017.2774831
Dan Wang , Witold Pedrycz , Zhiwu Li

The design of information granules assumes a central position in the discipline of Granular Computing and its applications. The principle of justifiable granularity offers a conceptually and algorithmically attractive way of designing information granule completed on a basis of some experimental evidence (especially present in the form of numeric data). This paper builds upon the existing principle and presents its significant generalization, referred here as an adaptive principle of justifiable information granularity. The method supports a granular data aggregation producing an optimal information granule (with the optimality expressed in terms of the criteria of coverage and specificity commonly used when characterizing quality of information granules). The flexibility of the method stems from an introduction of the adaptive weighting scheme of the data leading to a vector of weights used in the construction of the optimal information granule. A detailed design procedure is provided along with the required optimization vehicle (realized with the aid of the population-based optimization techniques, such as particle swarm optimization and differential evolution). Two direct application areas in which the principle becomes of direct usage include prediction of time series and prediction of spatial data. In both cases, it is advocated that the results formed by the principle are reflective of the precision (quality) of the prediction process.

中文翻译:

粒度数据聚合:合理粒度方法的自适应原理

信息颗粒的设计在颗粒计算及其应用学科中占据中心位置。合理粒度的原理为设计基于某些实验证据(尤其是以数字数据形式存在)的信息颗粒提供了一种概念上和算法上有吸引力的方式。本文建立在现有原则的基础上,并提出了其重要的概括,在这里被称为合理信息粒度的自适应原则。该方法支持产生最佳信息颗粒的粒状数据聚合(具有在表征信息颗粒质量时通常使用的覆盖率和特异性标准表示的最优性)。该方法的灵活性源于对数据的自适应加权方案的引入,该自适应加权方案导致了用于构建最佳信息颗粒的权重向量。提供了详细的设计过程以及所需的优化工具(借助于基于总体的优化技术(例如粒子群优化和差分进化)来实现)。原理成为直接使用的两个直接应用领域包括时间序列的预测和空间数据的预测。在两种情况下,都主张该原理形成的结果反映了预测过程的精度(质量)。提供了详细的设计过程以及所需的优化工具(借助于基于总体的优化技术(例如粒子群优化和差分进化)来实现)。原理成为直接使用的两个直接应用领域包括时间序列的预测和空间数据的预测。在两种情况下,都主张该原理形成的结果反映了预测过程的精度(质量)。提供了详细的设计过程以及所需的优化工具(借助于基于总体的优化技术(例如粒子群优化和差分进化)来实现)。原理成为直接使用的两个直接应用领域包括时间序列的预测和空间数据的预测。在两种情况下,都主张该原理形成的结果反映了预测过程的精度(质量)。
更新日期:2019-02-01
down
wechat
bug