Machine learning is better than human to satisfy decision by majority

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

9 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルProceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
出版社Association for Computing Machinery, Inc
ページ694-701
ページ数8
ISBN(電子版)9781450349512
DOI
出版ステータス出版済み - 8 23 2017
イベント16th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017 - Leipzig, ドイツ
継続期間: 8 23 20178 26 2017

出版物シリーズ

名前Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017

その他

その他16th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
国/地域ドイツ
CityLeipzig
Period8/23/178/26/17

All Science Journal Classification (ASJC) codes

  • コンピュータ ネットワークおよび通信
  • 人工知能
  • ソフトウェア

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