DocumentCode :
3575395
Title :
Evolving Fair Linear Regression for the Representation of Human-Drawn Regression Lines
Author :
Koeppen, Mario ; Yoshida, Kaori ; Ohnishi, Kei
Author_Institution :
Grad. Sch. of Creative Inf., Kyushu Inst. of Technol., Fukuoka, Japan
fYear :
2014
Firstpage :
296
Lastpage :
303
Abstract :
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.
Keywords :
decision making; regression analysis; data points; differential evolution; evolutionary computation; fair linear regression evolving; general fairness relation; human decision making process; human-drawn regression line representation; Linear regression; Open wireless architecture; Optimization; Resource management; Sorting; Syntactics; Vectors; differential evolution; fairness; linear regression; ordered weighted averaging;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Networking and Collaborative Systems (INCoS), 2014 International Conference on
Print_ISBN :
978-1-4799-6386-7
Type :
conf
DOI :
10.1109/INCoS.2014.89
Filename :
7057105
Link To Document :
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