Hierarchical Dimensionless Method Based on Data Distribution Characteristics and Its Equilibrium Analysis

被引：0

作者：

Yi P.-T. ^{[1
]}

Yuan J.-R. ^{[1
]}

Li W.-W. ^{[1
]}

机构：

[1] School of Business Administration, Northeastern University, Shenyang

来源：

Dongbei Daxue Xuebao/Journal of Northeastern University | 2023年 / 44卷 / 06期

关键词：

data density; dimensionless method; hierarchical dimensionless method; objective hierarchy; outlier;

D O I：

10.12068/j.issn.1005-3026.2023.06.017

中图分类号：

学科分类号：

摘要：

The hierarchical dimensionless method can effectively remove the effect of different index dimensions, and solve imbalanced data distribution and low discrimination caused by anomalous index values. However, when using this method, it is necessary to artificially specify the number of partition intervals so that the dimensionless results are interfered by human factors and lose objectivity. To solve this problem, a dimensionless method of density hierarchy is proposed considering the distribution characteristics of raw data. This method divides the interval according to the density of data distribution, objectively determines the hierarchical series, and takes into account the advantages of the hierarchical dimensionless method. The calculation is comparatively simple and reduces human factors. In addition, through the stochastic simulation method, it is found that the method has good anti-interference to outliers, and the balance of dimensionless results is affected by the scale of raw data. © 2023 Northeastern University. All rights reserved.

引用

页码：889 / 897

页数：8