Distance-based one-class time-series classification approach using local cluster balance
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2024/01/01
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Journal
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Indian UGC (journal)
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Refrences52
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Citations2
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Toshitaka Hayashi ORCID (unauthenticated)
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Dalibor Cimr ORCID (unauthenticated)
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Filip Studnička ORCID (unauthenticated)
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Hamido Fujita ORCID (unauthenticated)
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Damián Bušovský ORCID (unauthenticated)
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Richard Cimler ORCID (unauthenticated)
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Ali Selamat
Cite
Hayashi, Toshitaka, et al. “Distance-Based One-Class Time-Series Classification Approach Using Local Cluster Balance”. Expert Systems With Applications, vol. 235, 2024, p. 121201, https://doi.org/10.1016/j.eswa.2023.121201.
Hayashi, T., Cimr, D., Studnička, F., Fujita, H., Bušovský, D., Cimler, R., & Selamat, A. (2024). Distance-based one-class time-series classification approach using local cluster balance. Expert Systems With Applications, 235, 121201. https://doi.org/10.1016/j.eswa.2023.121201
Hayashi, Toshitaka, Dalibor Cimr, Filip Studnička, Hamido Fujita, Damián Bušovský, Richard Cimler, and Ali Selamat. “Distance-Based One-Class Time-Series Classification Approach Using Local Cluster Balance”. Expert Systems With Applications 235 (2024): 121201. https://doi.org/10.1016/j.eswa.2023.121201.
Hayashi T, Cimr D, Studnička F, Fujita H, Bušovský D, Cimler R, et al. Distance-based one-class time-series classification approach using local cluster balance. Expert Systems with Applications. 2024;235:121201.
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| OCFSP: self-supervised one-class classification approach using feature-slide prediction subtask for feature data | Soft Computing |
| 2 | 2022 |
Anomaly Detection Using Signal Segmentation and One-Class Classification in Diffusion Process of Semiconductor Manufacturing | Sensors |
| 6 | 2021 |
| Learning Neural Representations for Network Anomaly Detection | 2019 | |||
| A review on distance based time series classification | Data Mining and Knowledge Discovery |
| 93 | 2018 |
| Relationship between Variants of One-Class Nearest Neighbors and Creating Their Accurate Ensembles | 2018 |
Refrences Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 43
is the most frequently represented among the references in this article. It primarily includes studies from Expert Systems with Applications and Information Sciences. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Interpretable synthetic signals for explainable one-class time-series classification | Engineering Applications of Artificial Intelligence |
| 2024 | |
| Patient deterioration detection using one-class classification via cluster period estimation subtask | Information Sciences |
| 2024 |
Citations Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 2
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Interpretable synthetic signals for explainable one-class time-series classification and was published in 2024. The most recent citation comes from a 2024 study titled Interpretable synthetic signals for explainable one-class time-series classification. This article reached its peak citation in 2024, with 2 citations. It has been cited in 2 different journals. Among related journals, the Engineering Applications of Artificial Intelligence cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.