Distance-based one-class time-series classification approach using local cluster balance

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F24%3A50020644" target="_blank" >RIV/62690094:18470/24:50020644 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0957417423017037" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417423017037</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2023.121201" target="_blank" >10.1016/j.eswa.2023.121201</a>

Result language

angličtina

Original language name

Distance-based one-class time-series classification approach using local cluster balance

Original language description

Deciding the signal length is an important challenge for one-class time-series classification (OCTSC). This paper aims to develop an OCTSC algorithm that does not require model retraining for different signal lengths. For this purpose, a distance-based one-class time-series classification approach using local cluster balance (OCLCB) is proposed. OCLCB extracts feature vectors, namely, local cluster balance (LCB), from the clustering results of sliding windows. K-means clustering is applied to the sliding windows extracted from the training signal. Then, the local prototype (LP) is calculated as the average of the local cluster balance (LCB) in the training data. Unseen scores are computed as the distance metrics between LP and LCBs in the testing data. Since the sliding window size is independent of the entire signal size, OCLCB does not need to retrain the model. This aspect gives the benefit of reducing the parameter tuning costs. The source code is uploaded at https://github.com/ToshiHayash i/OCLCB.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Expert systems with applications

ISSN

0957-4174

e-ISSN

1873-6793

Volume of the periodical

235

Issue of the periodical within the volume

January

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

"Article Number: 121201"

UT code for WoS article

001059416500001

EID of the result in the Scopus database

2-s2.0-85168558271