Evolving Fair Linear Regression for the Representation of Human-Drawn Regression Lines
Evolving Fair Linear Regression for the Representation of Human-Drawn Regression Lines
复制标题
用于表示人工绘制回归线的演化公平线性回归
DOI：
10.1109/incos.2014.89
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
K. Ohnishi
中科院分区：
文献类型：
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作者：
M. Köppen;Kaori Yoshida;K. Ohnishi
在这里，我们研究线性回归到一般公平关系的极大元情形的推广。然后，回归是基于平衡到数据点的距离。所研究的关系是词典最小值、最大最小公平性、比例公平性和多数，所有这些关系都在一个互补的版本中表示最小性。还引入了比例公平和多数的新组合。实验是在解决视觉任务的人类受试者上进行的，以绘制一条适合给定数据点的线，并通过使用进化计算(这里是通过差异进化)将公平线性回归的权重调整为人类提供的结果。这一事实提供了比(加权)线性回归更精确的近似值，这暗示着在人类决策过程中包含了到给定数据点的距离之间的平衡。
Here we study a generalization of linear regression to the case of maximal elements of a general fairness relation. The regression then is based on balancing the distances to the data points. The studied relations are lexicographic minimum, maxmin fairness, proportional fairness, and majorities, all in a complementary version to represent minimality. A new combination of proportional fairness and majority is introduced as well. Experiments are performed on human subjects solving the visual task to draw a line fitting to given data points, and by use of evolutionary computation (here by Differential Evolution) the weights of a fair linear regression are adjusted to the human-provided results. The fact that this gives a more precise approximation than (weighted) linear regression hints on the inclusion of the balance among the distances to the given data points in the human decision making process.
DOI：
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发表时间：
2011
期刊：
影响因子：
--
作者：
Miroslaw Makohonienko;Hiroyuki Kitagawa;Toshiyuki Fujiki;Xin Liu;Yoshinori Yasuda;Huaining Yin;劉浩;Mario Koppen
通讯作者：
Mario Koppen