Constrained center and range joint model for interval-valued symbolic data regression
Constrained center and range joint model for interval-valued symbolic data regression
复制标题
区间值符号数据回归的约束中心和范围联合模型
DOI：
10.1016/j.csda.2017.06.005
复制
发表时间：
2017-12-01
影响因子：
1.8
通讯作者：
Guo, Junpeng
中科院分区：
文献类型：
--
作者：
Hao, Peng;Guo, Junpeng
提出了一种约束中心和极差联合模型，用于区间值符号数据的线性回归拟合。该方法同时利用区间的中心和极差来拟合线性回归模型，并通过增加非负约束来避免预测的因变量区间的极差为负值。为了提高预测精度，它采用重叠约束。利用一点代数知识，将其构造为不等式最小二乘问题的一种特殊情况，并利用Matlab程序求解。所提出的预测方法的评估是基于估计的平均均方根误差和准确率。在蒙特卡罗实验的框架中，不同的数据集配置考虑到误差的丰富或缺乏，以及相对于因变量和自变量的斜率。统计t检验比较了新模型的性能与四个以前报道的方法。实验结果表明，该模型具有较好的适应性.对离群值进行分析，以确定离群值对我们的建议的影响。所提出的方法说明了从两个现实生活中的案例研究的数据分析，比较其性能与其他方法。(C)2017爱思唯尔B.V.保留所有权利。
A constrained center and range joint model to fit linear regression to interval-valued symbolic data is introduced. This new method applies both the center and range of the interval to fit a linear regression model, and avoids the negative value of the range of the predicted dependent interval variable by adding nonnegative constraints. To improve prediction accuracy it adopts overlapping constraints. Using a little algebra, it is constructed as a special case of the least squares with inequality (LSI) problem and is solved with a Matlab routine. The assessment of the proposed prediction method is based on an estimation of the average root mean square error and accuracy rate. In the framework of a Monte Carlo experiment, different data set configurations take into account the rich or lack of error, as well as the slope with respect to the dependent and independent variables. A statistical t-test compares the performance of the new model with that of four previously reported methods. Based on experiment results, it is outlined that the new model has better fitness. An analysis of outliers is performed to determine the effects of outliers on our proposal. The proposed method is illustrated by analyses of data from two real-life case studies to compare its performance with those of the other methods. (C) 2017 Elsevier B.V. All rights reserved.