OCFSP: self-supervised one-class classification approach using feature-slide prediction subtask for feature data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50020122" target="_blank" >RIV/62690094:18470/22:50020122 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s00500-022-07414-z" target="_blank" >https://link.springer.com/article/10.1007/s00500-022-07414-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00500-022-07414-z" target="_blank" >10.1007/s00500-022-07414-z</a>

Jazyk výsledku

angličtina

Název v původním jazyce

OCFSP: self-supervised one-class classification approach using feature-slide prediction subtask for feature data

Popis výsledku v původním jazyce

One-class classification (OCC) is a machine learning problem where training data has only one class. Recently, self-supervised OCC algorithms have been increasing attention. These algorithms train the model for pretext tasks and use the model error for OCC. However, these tasks are specialized for images, and applying them to feature data is not practical or appropriate for such a purpose. The motivation of this study is to apply self-supervised OCC to feature data. For this purpose, this paper proposes an OCC approach using feature-slide prediction (FSP) subtask for feature data (OCFSP). The main originality is the FSP subtask, which is the first classification subtask for feature data. In particular, the proposed method creates a self-labeled dataset by generating additional feature vectors with the feature slide of original vectors and self-annotating these vectors as the number of the slides. Such a dataset is applied to train a multi-class classifier to predict the number of feature slides. Since this classification model learns data from only one class, the FSP accuracy for a seen class is higher relative to unseen classes. Accordingly, OCC could be made using the accuracy of FSP. The proposed methods are experimented with using the imbalanced-learn, covtype, and kddcup datasets. OCFSP shows fair accuracy where few training data is given. In addition, classification subtask for feature data shows a relatively fast testing speed, unlike image data. Therefore, the bottleneck of the self-supervised approach is considered the memory size, which is the main difference between image and feature data. Source code is uploaded at https://github.com/ToshiHayashi/OCFSP

Název v anglickém jazyce

OCFSP: self-supervised one-class classification approach using feature-slide prediction subtask for feature data

Popis výsledku anglicky

One-class classification (OCC) is a machine learning problem where training data has only one class. Recently, self-supervised OCC algorithms have been increasing attention. These algorithms train the model for pretext tasks and use the model error for OCC. However, these tasks are specialized for images, and applying them to feature data is not practical or appropriate for such a purpose. The motivation of this study is to apply self-supervised OCC to feature data. For this purpose, this paper proposes an OCC approach using feature-slide prediction (FSP) subtask for feature data (OCFSP). The main originality is the FSP subtask, which is the first classification subtask for feature data. In particular, the proposed method creates a self-labeled dataset by generating additional feature vectors with the feature slide of original vectors and self-annotating these vectors as the number of the slides. Such a dataset is applied to train a multi-class classifier to predict the number of feature slides. Since this classification model learns data from only one class, the FSP accuracy for a seen class is higher relative to unseen classes. Accordingly, OCC could be made using the accuracy of FSP. The proposed methods are experimented with using the imbalanced-learn, covtype, and kddcup datasets. OCFSP shows fair accuracy where few training data is given. In addition, classification subtask for feature data shows a relatively fast testing speed, unlike image data. Therefore, the bottleneck of the self-supervised approach is considered the memory size, which is the main difference between image and feature data. Source code is uploaded at https://github.com/ToshiHayashi/OCFSP

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Soft Computing

ISSN

1432-7643

e-ISSN

1433-7479

Svazek periodika

26

Číslo periodika v rámci svazku

19

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

10127-10149

Kód UT WoS článku

000838499600003

EID výsledku v databázi Scopus

2-s2.0-85136922796