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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Information sciences

ISSN

0020-0255

e-ISSN

1872-6291

Svazek periodika

614

Číslo periodika v rámci svazku

October

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

71-86

Kód UT WoS článku

000911596800002

EID výsledku v databázi Scopus

2-s2.0-85139590778