Multivariate Time Series Open-Set Recognition Using Multi-Feature Extraction and Reconstruction

Hyeryeong Oh, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

In real-world classification tasks, deep neural networks show innovative performance in various fields. However, traditional classification methods are constructed based on a set of predefined classes and force unknown classes that determine their categorization into one of the predefined classes. This problem is addressed by the research field known as open-set recognition. Existing open-set recognition methods claim that the unique features of unknowns cannot be maintained by using only the final features. In other words, the various feature extraction methods should be considered to effectively reflect the characteristics of unknowns. In this study, we propose an open-set recognition model equipped with multi-feature extraction for multivariate time series data. The results of experiments with various multivariate time series datasets indicate that the proposed method shows improved capability to detect unknown classes while maintaining good predictive performance.

Original languageEnglish
Pages (from-to)120063-120073
Number of pages11
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 2022

Bibliographical note

Funding Information:
This work was supported in part by the Brain Korea 21 FOUR, the Ministry of Science and ICT (MSIT) in Korea under the ITRC Support Program supervised by the Institute for Information Communication Technology Planning and Evaluation, under Grant IITP-2020-0-01749; and in part by the National Research Foundation of Korea Grant through the Korea Government under Grant RS-2022-00144190.

Publisher Copyright:
© 2013 IEEE.

Keywords

  • multichannel signal
  • multisensor signal
  • multivariate time series
  • Open-set recognition
  • reconstruction error

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering

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