A hybrid memetic algorithm for simultaneously selecting features and instances in big industrial iot data for predictive maintenance

Yu Lin Liang, Chih Chi Kuo, Chun Cheng Lin

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

1 Scopus citations

Abstract

In Industry 4.0, various types of IoT sensors which are installed on machines to collect data for predictive maintenance. As the collected data increases, there are more missing values and noisy data. Related studies have already proposed various methods to solve the problems in big data. Among them, most studies focused on either feature selection or instance selection for data preprocessing before training forecast models. Metaheuristic algorithm is one of the mainstream methods in data preprocessing. However, most of these studies rarely considered feature and instance selection simultaneously. In addition, they seldom focused on noisy data. Therefore, this work combines the UCI datasets with noisy data to simulate the real situation. Memetic algorithm (MA) has excellent performance in machine learning of data selection, and variable neighborhood search (VNS) was also proved to be widely applied to the systematic change of local search algorithms. This work proposes a hybrid MA and VNS to find a new subset that maximizes the accuracy of the classifier while preserving the minimum amount of data by feature and instance selection simultaneously. Experimental results show that the proposed method can efficiently reduce the amount of data and the ratio of noisy data. By comparison with other metaheuristic algorithms, the proposed method has good performance by an excellent balance between exploration and exploitation.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1266-1270
Number of pages5
ISBN (Electronic)9781728129273
DOIs
StatePublished - Jul 2019
Event17th IEEE International Conference on Industrial Informatics, INDIN 2019 - Helsinki-Espoo, Finland
Duration: 22 Jul 201925 Jul 2019

Publication series

NameIEEE International Conference on Industrial Informatics (INDIN)
Volume2019-July
ISSN (Print)1935-4576

Conference

Conference17th IEEE International Conference on Industrial Informatics, INDIN 2019
CountryFinland
CityHelsinki-Espoo
Period22/07/1925/07/19

Keywords

  • Big data
  • Evolutionary computation
  • Feature selection
  • Instance selection
  • Machine learning
  • Noisy data

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    Liang, Y. L., Kuo, C. C., & Lin, C. C. (2019). A hybrid memetic algorithm for simultaneously selecting features and instances in big industrial iot data for predictive maintenance. In Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019 (pp. 1266-1270). [8972199] (IEEE International Conference on Industrial Informatics (INDIN); Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INDIN41052.2019.8972199