Smart city analytics: ensemble-learned prediction of citizen home care
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Smart city analytics : ensemble-learned prediction of citizen home care. / Hansen, Casper; Hansen, Christian; Alstrup, Stephen; Lioma, Christina.
Proceedings of the 2017 ACM Conference on Information and Knowledge Management. Association for Computing Machinery, 2017. p. 2095-2098.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Smart city analytics
AU - Hansen, Casper
AU - Hansen, Christian
AU - Alstrup, Stephen
AU - Lioma, Christina
N1 - Conference code: 26
PY - 2017
Y1 - 2017
N2 - We present an ensemble learning method that predicts large increases in the hours of home care received by citizens. The method is supervised, and uses different ensembles of either linear (logistic regression) or non-linear (random forests) classifiers. Experiments with data available from 2013 to 2017 for every citizen in Copenhagen receiving home care (27,775 citizens) show that prediction can achieve state of the art performance as reported in similar health related domains (AUC=0.715). We further find that competitive results can be obtained by using limited information for training, which is very useful when full records are not accessible or available. Smart city analytics does not necessarily require full city records. To our knowledge this preliminary study is the first to predict large increases in home care for smart city analytics.
AB - We present an ensemble learning method that predicts large increases in the hours of home care received by citizens. The method is supervised, and uses different ensembles of either linear (logistic regression) or non-linear (random forests) classifiers. Experiments with data available from 2013 to 2017 for every citizen in Copenhagen receiving home care (27,775 citizens) show that prediction can achieve state of the art performance as reported in similar health related domains (AUC=0.715). We further find that competitive results can be obtained by using limited information for training, which is very useful when full records are not accessible or available. Smart city analytics does not necessarily require full city records. To our knowledge this preliminary study is the first to predict large increases in home care for smart city analytics.
KW - Ensemble learning
KW - Home care
KW - Smart city analytics
UR - http://www.scopus.com/inward/record.url?scp=85037344580&partnerID=8YFLogxK
U2 - 10.1145/3132847.3133101
DO - 10.1145/3132847.3133101
M3 - Article in proceedings
AN - SCOPUS:85037344580
SP - 2095
EP - 2098
BT - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 6 November 2017 through 10 November 2017
ER -
ID: 188363096