research-article

Machine learning is better than human to satisfy decision by majority

Authors:

Sachio Hirokawa,

Takahiko Suzuki,

Tsunenori MineAuthors Info & Claims

WI '17: Proceedings of the International Conference on Web Intelligence

Pages 694 - 701

https://doi.org/10.1145/3106426.3106520

Published: 23 August 2017 Publication History

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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.

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Cited By

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Yamaguchi KMine T(2021)Estimation of Precedence Relations to Deal with Regional Complaint Reports2021 IEEE International Conference on Agents (ICA)10.1109/ICA54137.2021.00008(7-12)Online publication date: Dec-2021
https://doi.org/10.1109/ICA54137.2021.00008
Nazar ILiyanage YZois DChelmis C(2020)Sequential Heterogeneous Feature Selection for Multi–Class Classification: Application in Government 2.02020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)10.1109/MLSP49062.2020.9231767(1-6)Online publication date: Sep-2020
https://doi.org/10.1109/MLSP49062.2020.9231767
Nazar IZois DChelmis C(2020)Knowing When to Stop: Joint Heterogeneous Feature Selection and Classification2020 54th Asilomar Conference on Signals, Systems, and Computers10.1109/IEEECONF51394.2020.9443288(1227-1231)Online publication date: 1-Nov-2020
https://doi.org/10.1109/IEEECONF51394.2020.9443288
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Index Terms

Machine learning is better than human to satisfy decision by majority
1. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection

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Information & Contributors

Information

Published In

WI '17: Proceedings of the International Conference on Web Intelligence

August 2017

1284 pages

ISBN:9781450349512

DOI:10.1145/3106426

Conference Chair:
Amit Sheth
Wright State University GuoROLE@GENERAL CHAIR
,
General Chairs:
Axel Ngonga
Leipzig University, Germany
,
yin Wang
Chongqing University of Posts and Telecommunications, China
,
Elizabeth Chang
The University of New South Wales, Australia
,
Dominik Ślęzak
Infobright Inc. & University of Warsaw, Poland
,
Bogdan Franczyk
Leipzig University, Germany
,
Program Chairs:
Rainer Alt
Leipzig University, Germany
,
Xiaohui Tao
University of Southern Queensland, Australia

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2017

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Research-article

Funding Sources

JSPS KAKENHI

Conference

WI '17

Sponsor:

SIGAI
TCII

WI '17: International Conference on Web Intelligence 2017

August 23 - 26, 2017

Leipzig, Germany

Acceptance Rates

WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;

Overall Acceptance Rate 118 of 178 submissions, 66%

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Cited By

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Yamaguchi KMine T(2021)Estimation of Precedence Relations to Deal with Regional Complaint Reports2021 IEEE International Conference on Agents (ICA)10.1109/ICA54137.2021.00008(7-12)Online publication date: Dec-2021
https://doi.org/10.1109/ICA54137.2021.00008
Nazar ILiyanage YZois DChelmis C(2020)Sequential Heterogeneous Feature Selection for Multi–Class Classification: Application in Government 2.02020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)10.1109/MLSP49062.2020.9231767(1-6)Online publication date: Sep-2020
https://doi.org/10.1109/MLSP49062.2020.9231767
Nazar IZois DChelmis C(2020)Knowing When to Stop: Joint Heterogeneous Feature Selection and Classification2020 54th Asilomar Conference on Signals, Systems, and Computers10.1109/IEEECONF51394.2020.9443288(1227-1231)Online publication date: 1-Nov-2020
https://doi.org/10.1109/IEEECONF51394.2020.9443288
Liyanage YZois DChelmis C(2020)On–The–Fly Feature Selection and Classification with Application to Civic Engagement PlatformsICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP40776.2020.9053564(3762-3766)Online publication date: May-2020
https://doi.org/10.1109/ICASSP40776.2020.9053564
Alexopoulos CLachana ZAndroutsopoulou ADiamantopoulou VCharalabidis YLoutsaris M(2019)How Machine Learning is Changing e-GovernmentProceedings of the 12th International Conference on Theory and Practice of Electronic Governance10.1145/3326365.3326412(354-363)Online publication date: 3-Apr-2019
https://dl.acm.org/doi/10.1145/3326365.3326412
Nazar ILiyanage YZois DChelmis C(2019)Automated Optimal Online Civil Issue Classification using Multiple Feature Sets2019 53rd Asilomar Conference on Signals, Systems, and Computers10.1109/IEEECONF44664.2019.9048766(1591-1595)Online publication date: Nov-2019
https://doi.org/10.1109/IEEECONF44664.2019.9048766
Liyanage YZois DChelmis CYao M(2019)Automating the Classification of Urban Issue Reports: an Optimal Stopping ApproachICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2019.8682778(3137-3141)Online publication date: May-2019
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Liyanage YYao MYong CZois DChelmis C(2018)WHAT MATTERS THE MOST? OPTIMAL QUICK CLASSIFICATION OF URBAN ISSUE REPORTS BY IMPORTANCE2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)10.1109/GlobalSIP.2018.8646639(106-110)Online publication date: Nov-2018
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Lin YYamaguchi KMine THirokawa S(2018)Is SVM+FS Better to Satisfy Decision by Majority?Recent Advances on Soft Computing and Data Mining10.1007/978-3-319-72550-5_26(261-271)Online publication date: 12-Jan-2018
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