Knowledge discovery and variable scale evaluation for long series data-Reference-Cited by-同舟云学术

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Knowledge discovery and variable scale evaluation for long series data

Published:2022-08-24 Issue:4 Volume:56 Page:3157-3180
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Zhai Yanwei,Lv Zheng^ORCID,Zhao Jun,Wang Wei

Funder

Fundamental Research Funds for the Central Universities

National Key R&D Program of China under

National Natural Sciences Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-022-10250-0.pdf

Reference40 articles.

1. Alzubaidi A, Tepper J, Lotfi A (2020) A novel deep mining model for effective knowledge discovery from omics data. Artif Intell Med 104:101821

2. Azami H, Escudero J (2016) Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation. Comput Methods Programs Biomed 128:40–51

3. Baccigalupi A, Liccardo A (2016) The Huang Hilbert Transform for evaluating the instantaneous frequency evolution of transient signals in non-linear systems. Measurement 86:1–13

4. Bagnall A, Lines J, Hills J, Bostrom A (2015) Time-Series Classification with COTE: The collective of transformation-based ensembles. IEEE Trans Knowl Data Eng 27(9):2522–2535

5. Bao C, Wu D, Li J (2019) A knowledge-based risk measure from the fuzzy multicriteria decision-making perspective. IEEE Trans Fuzzy Syst 27(5):1126–1138

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