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CBRL and CBRC: Novel Algorithms for Improving Missing Value Imputation Accuracy Based on Bayesian Ridge Regression
Symmetry ( IF 2.940 ) Pub Date : 2020-09-25 , DOI: 10.3390/sym12101594
Samih M. Mostafa , Abdelrahman S. Eladimy , Safwat Hamad , Hirofumi Amano

In most scientific studies such as data analysis, the existence of missing data is a critical problem, and selecting the appropriate approach to deal with missing data is a challenge. In this paper, the authors perform a fair comparative study of some practical imputation methods used for handling missing values against two proposed imputation algorithms. The proposed algorithms depend on the Bayesian Ridge technique under two different feature selection conditions. The proposed algorithms differ from the existing approaches in that they cumulate the imputed features; those imputed features will be incorporated within the Bayesian Ridge equation for predicting the missing values in the next incomplete selected feature. The authors applied the proposed algorithms on eight datasets with different amount of missing values created from different missingness mechanisms. The performance was measured in terms of imputation time, root-mean-square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). The results showed that the performance varies depending on missing values percentage, size of the dataset, and the missingness mechanism. In addition, the performance of the proposed methods is slightly better.

中文翻译:

CBRL 和 CBRC:基于贝叶斯岭回归提高缺失值插补精度的新算法

在数据分析等大多数科学研究中,缺失数据的存在是一个关键问题,选择合适的方法来处理缺失数据是一个挑战。在本文中,作者对一些用于处理缺失值的实用插补方法与两种提议的插补算法进行了公平的比较研究。所提出的算法取决于两种不同特征选择条件下的贝叶斯岭技术。所提出的算法与现有方法的不同之处在于它们累积了估算的特征;这些估算的特征将被纳入贝叶斯岭方程,用于预测下一个不完整的选定特征中的缺失值。作者将所提出的算法应用于八个数据集,这些数据集具有由不同缺失机制创建的不同数量的缺失值。性能是根据插补时间、均方根误差 (RMSE)、决定系数 (R2) 和平均绝对误差 (MAE) 来衡量的。结果表明,性能因缺失值百分比、数据集大小和缺失机制而异。此外,所提出方法的性能略好。
更新日期:2020-09-25
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