Automating the Classification of Urban Issue Reports: an Optimal Stopping Approach

Empowering citizens to interact directly with their local governments through civic engagement platforms has emerged as an easy way to resolve urban issues. However, for authorities to manually process reported issues is both impractical and inefficient; accurate, online and near-real-time processin...

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Bibliographic Details
Published inICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 3137 - 3141
Main Authors Liyanage, Yasitha Warahena, Zois, Daphney-Stavroula, Chelmis, Charalampos, Yao, Mengfan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2019
Subjects
Civic engagement
classification
Computational complexity
Cost function
Dimensionality reduction
Feature extraction
government 2.0
Local government
optimal stopping theory
quickest detection
Random variables
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Summary:Empowering citizens to interact directly with their local governments through civic engagement platforms has emerged as an easy way to resolve urban issues. However, for authorities to manually process reported issues is both impractical and inefficient; accurate, online and near-real-time processing methods are necessary to maintain citizens' satisfaction with their local governments. Herein, an optimal stopping framework is proposed to process urban issue requests quickly and accurately. The optimal classification and stopping rules are derived, and significant reduction in time-to-decision without sacrificing accuracy is demonstrated on a real-world dataset from SeeClickFix.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8682778