TY - GEN
T1 - Greedy Features Quantity Selection Method from Multivariate Time Series Data for Customer Classification
AU - Kumoi, Gendo
AU - Goto, Masayuki
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In recent years, toward the realization of the Internet of Things ('IoT') society, related technologies have been developed and various electronic devices are being connected to the network. Even in companies that provide such kinds of products and services, it is possible to collect usage histories of their customers. If the companies can appropriately analyze the usage history data, it is useful for their marketing activities. However, in general, device usage histories are multivariate time-series data, and it is not obvious how to construct a feature space for customer classification and clustering. Therefore, this paper proposes a method to automatically select feature quantities characterizing the properties of customers using machine learning. We apply this method to real data and show its effectiveness.
AB - In recent years, toward the realization of the Internet of Things ('IoT') society, related technologies have been developed and various electronic devices are being connected to the network. Even in companies that provide such kinds of products and services, it is possible to collect usage histories of their customers. If the companies can appropriately analyze the usage history data, it is useful for their marketing activities. However, in general, device usage histories are multivariate time-series data, and it is not obvious how to construct a feature space for customer classification and clustering. Therefore, this paper proposes a method to automatically select feature quantities characterizing the properties of customers using machine learning. We apply this method to real data and show its effectiveness.
KW - Binary Classifier
KW - Feature Quantity Selection
KW - Multivariate Time Series Data
KW - Random Forest
UR - http://www.scopus.com/inward/record.url?scp=85075025856&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075025856&partnerID=8YFLogxK
U2 - 10.1109/BCD.2019.8885219
DO - 10.1109/BCD.2019.8885219
M3 - Conference contribution
AN - SCOPUS:85075025856
T3 - Proceedings - 2019 IEEE/ACIS 4th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019
SP - 154
EP - 159
BT - Proceedings - 2019 IEEE/ACIS 4th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019
A2 - Iwashita, Motoi
A2 - Shimoda, Atsushi
A2 - Chertchom, Prajak
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019
Y2 - 29 May 2019 through 31 May 2019
ER -