Proposal of Feature Value Selection Method for Time-Critical Learning

Kanami Yuyama, Hiroaki Nishi

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

Abstract

The development of IoT has led to the creation of a data-enriched environment that enables data gathering by using distributed sensors and terminals. However, in this environment, the cost of data analysis has increased. Machine learning has gained attention for reducing the cost because enabling automatic data analysis, as well as multidimensional data, is expected. However, for enormous data, such as Big Data, we still have to pay costs. Therefore, selecting feature values when using machine learning technology is essential, especially as inputs of a classifier. Selecting the feature values increases its estimation accuracy. Moreover, the time cost, as well as calculation cost, needs consideration for the actual time-critical use of machine learning, especially in its learning process. Therefore, in this study, we proposed an algorithm that selected suitable feature values in required time. The proposed method consists of two stages: stepwise input selection stage using ANOVA and feature deletion stage according to the contribution rate of the features to estimate accuracy. These selection and deletion processes continue until the required processing time. We confirmed the efficiency of the proposed method by using an environment of a crystallization process in a factory and a household's occupancy estimation. A comparison with the original stepwise input method proved that the proposed method improved the estimation accuracy by 2% and 5% in the estimation of the substance amount of the crystallization process and household's occupancy, respectively.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation, ETFA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1365-1371
Number of pages7
Volume2018-September
ISBN (Electronic)9781538671085
DOIs
Publication statusPublished - 2018 Oct 22
Event23rd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2018 - Torino, Italy
Duration: 2018 Sep 42018 Sep 7

Other

Other23rd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2018
CountryItaly
CityTorino
Period18/9/418/9/7

Keywords

  • ANOVA
  • Feature value selection
  • k-NN
  • real-time feature selection
  • time-constraint machine learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Computer Science Applications

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  • Cite this

    Yuyama, K., & Nishi, H. (2018). Proposal of Feature Value Selection Method for Time-Critical Learning. In Proceedings - 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation, ETFA 2018 (Vol. 2018-September, pp. 1365-1371). [8502622] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ETFA.2018.8502622