Voting Rules As Error-Correcting Codes

被引：0
|
作者：
Procaccia, Ariel D. [1 ]
Shah, Nisarg [1 ]
Zick, Yair [1 ]
机构：
[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
来源：
PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2015年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present the first model of optimal voting under adversarial noise. From this viewpoint, voting rules are seen as error-correcting codes: their goal is to correct errors in the input rankings and recover a ranking that is close to the ground truth. We derive worst-case bounds on the relation between the average accuracy of the input votes, and the accuracy of the output ranking. Empirical results from real data show that our approach produces significantly more accurate rankings than alternative approaches.
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页码：1000 / 1006
页数：7
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