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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/ACIS 4th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019
EditorsMotoi Iwashita, Atsushi Shimoda, Prajak Chertchom
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages154-159
Number of pages6
ISBN (Electronic)9781728108865
DOIs
Publication statusPublished - 2019 May
Event4th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019 - Honolulu, United States
Duration: 2019 May 292019 May 31

Publication series

NameProceedings - 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

Keywords

  • Binary Classifier
  • Feature Quantity Selection
  • Multivariate Time Series Data
  • Random Forest

ASJC Scopus subject areas

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

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  • Cite this

    Kumoi, G., & Goto, M. (2019). Greedy Features Quantity Selection Method from Multivariate Time Series Data for Customer Classification. In M. Iwashita, A. Shimoda, & P. Chertchom (Eds.), Proceedings - 2019 IEEE/ACIS 4th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019 (pp. 154-159). [8885219] (Proceedings - 2019 IEEE/ACIS 4th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BCD.2019.8885219