OCSTN: One-class time-series classification approach using a signal transformation network into a goal signal

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019525" target="_blank" >RIV/62690094:18470/22:50019525 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/abs/pii/S0020025522010714" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0020025522010714</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ins.2022.09.027" target="_blank" >10.1016/j.ins.2022.09.027</a>

Result language
angličtina
Original language name
OCSTN: One-class time-series classification approach using a signal transformation network into a goal signal
Original language description
One-class classification (OCC) is a classification task where the training data have only one class. The goal is to classify input data into one seen class or other unseen classes. This paper proposes an OCC approach using a signal transformation network (OCSTN), which aims to process univariate time-series data. The main contribution is developing a signal transformation network (STN) that aims to transform input signals into one signal, namely the goal signal. Moreover, the model error of the STN is a distance metric between the goal signal and the model output. The STN model learns from one-class signals. Therefore, model error for one class is small relative to other classes. Accordingly, OCSTN could discriminate between seen and unseen classes using the model errors. The proposed OCSTN is evaluated using two ballistocardiography (BCG) datasets. The OCSTN achieves fair results in both AUC scores and processing speed. OCSTN has a weak point in training diverse signals. In addition, the entropy and smoothness of the goal signal are highly related to the AUC score.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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