Machine learning is better than human to satisfy decision by majority

研究成果: 著書/レポートタイプへの貢献会議での発言

4 引用 (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.

元の言語英語
ホスト出版物のタイトルProceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
出版者Association for Computing Machinery, Inc
ページ694-701
ページ数8
ISBN(電子版)9781450349512
DOI
出版物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
ドイツ
Leipzig
期間8/23/178/26/17

Fingerprint

Learning systems
Feature extraction

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Artificial Intelligence
  • Software

これを引用

Hirokawa, S., Suzuki, T., & Mine, T. (2017). Machine learning is better than human to satisfy decision by majority. : Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017 (pp. 694-701). (Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017). Association for Computing Machinery, Inc. https://doi.org/10.1145/3106426.3106520

Machine learning is better than human to satisfy decision by majority. / Hirokawa, Sachio; Suzuki, Takahiko; Mine, Tsunenori.

Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017. Association for Computing Machinery, Inc, 2017. p. 694-701 (Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017).

研究成果: 著書/レポートタイプへの貢献会議での発言

Hirokawa, S, Suzuki, T & Mine, T 2017, Machine learning is better than human to satisfy decision by majority. : Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017. Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017, Association for Computing Machinery, Inc, pp. 694-701, 16th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017, Leipzig, ドイツ, 8/23/17. https://doi.org/10.1145/3106426.3106520
Hirokawa S, Suzuki T, Mine T. Machine learning is better than human to satisfy decision by majority. : Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017. Association for Computing Machinery, Inc. 2017. p. 694-701. (Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017). https://doi.org/10.1145/3106426.3106520
Hirokawa, Sachio ; Suzuki, Takahiko ; Mine, Tsunenori. / Machine learning is better than human to satisfy decision by majority. Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017. Association for Computing Machinery, Inc, 2017. pp. 694-701 (Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017).
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