Machine learning is better than human to satisfy decision by majority

S. Hirokawa, Takahiko Suzuki, Tsunenori Mine
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引用次数: 9

Abstract

Government 2.0 activities have become very attractive and popular these days. Using platforms to support the activities, anyone can anytime report issues or complaints in a city with their photographs and geographical information on the Web, and share them with other people. Since a variety of reports are posted, officials in the city management section have to check the importance of each report and sort out their priorities to the reports. However, it is not easy task to judge the importance of the reports. When several officials work on the task, the agreement rate of their judgments is not always high. Even if the task is done by only one official, his/her judgment sometimes varies on a similar report. To remedy this low agreement rate problem of human judgments, we propose a method of detecting signs of danger or unsafe problems described in citizens' reports. The proposed method uses a machine learning technique with word feature selection. Experimental results clearly explain the low agreement rate of human judgments, and illustrate that the proposed machine learning method has much higher performance than human judgments.
机器学习比人类更能满足多数人的决策
政府2.0活动最近变得非常有吸引力和流行。利用平台支持活动,任何人都可以随时在网络上用自己的照片和地理信息报告城市的问题或投诉,并与他人分享。由于各种各样的报告被张贴,城市管理部门的官员必须检查每个报告的重要性,并对报告进行排序。然而,要判断这些报告的重要性并不容易。当几个官员共同完成一项任务时,他们判断的一致性并不总是很高。即使这项任务只由一个官员完成,他/她的判断有时也会因类似的报告而发生变化。为了纠正这种人类判断的低一致性问题,我们提出了一种检测公民报告中描述的危险或不安全问题迹象的方法。该方法使用了带有单词特征选择的机器学习技术。实验结果清楚地解释了人类判断的低符合率,并说明所提出的机器学习方法具有比人类判断更高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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