Greedy Features Quantity Selection Method from Multivariate Time Series Data for Customer Classification

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 2019 IEEE/ACIS 4th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019
編集者Motoi Iwashita, Atsushi Shimoda, Prajak Chertchom
出版社Institute of Electrical and Electronics Engineers Inc.
ページ154-159
ページ数6
ISBN(電子版)9781728108865
DOI
出版ステータスPublished - 2019 5
イベント4th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019 - Honolulu, United States
継続期間: 2019 5 292019 5 31

出版物シリーズ

名前Proceedings - 2019 IEEE/ACIS 4th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019

Conference

Conference4th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019
CountryUnited States
CityHonolulu
Period19/5/2919/5/31

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems and Management

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