The fusion of hyperparameter candidates for one-class classification problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F25%3A50022511" target="_blank" >RIV/62690094:18470/25:50022511 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0020025525006589?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0020025525006589?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ins.2025.122526" target="_blank" >10.1016/j.ins.2025.122526</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The fusion of hyperparameter candidates for one-class classification problems

Popis výsledku v původním jazyce

One-class classification (OCC) is a supervised classification problem where the training data is solely one class. OCC cannot execute hyperparameter tuning because its evaluation requires access to other classes; the algorithm will no longer be OCC if the model is updated after accessing other classes. To address this issue, this paper proposes hyperparameter fusion, which is applicable without the evaluation. The fusion process applies ensemble learning techniques, voting, and stacking into OCC models trained on different hyperparameters. The experiments involve 54 OCC problems from 27 imbalanced learn datasets and 115 hyperparameter candidates. The experiment results show that hyperparameter fusion outperformed the average base learners in the area under the receiver operating characteristic (AUC) score. Moreover, removing the worst base learner can improve the AUC score for the ensemble. The discussion section predicts the worst base learner from correlations of normality rankings created by model outputs. The worst base learner has relatively small ranking correlations to the ensemble model compared to other base learners.

Název v anglickém jazyce

The fusion of hyperparameter candidates for one-class classification problems

Popis výsledku anglicky

One-class classification (OCC) is a supervised classification problem where the training data is solely one class. OCC cannot execute hyperparameter tuning because its evaluation requires access to other classes; the algorithm will no longer be OCC if the model is updated after accessing other classes. To address this issue, this paper proposes hyperparameter fusion, which is applicable without the evaluation. The fusion process applies ensemble learning techniques, voting, and stacking into OCC models trained on different hyperparameters. The experiments involve 54 OCC problems from 27 imbalanced learn datasets and 115 hyperparameter candidates. The experiment results show that hyperparameter fusion outperformed the average base learners in the area under the receiver operating characteristic (AUC) score. Moreover, removing the worst base learner can improve the AUC score for the ensemble. The discussion section predicts the worst base learner from correlations of normality rankings created by model outputs. The worst base learner has relatively small ranking correlations to the ensemble model compared to other base learners.

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

<a href="/cs/project/EH22_008%2F0004583" target="_blank" >EH22_008/0004583: Excelentní výzkum v oblasti digitálních technologií a wellbeingu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

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

720

Číslo periodika v rámci svazku

December

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

"Article Number: 122526"

Kód UT WoS článku

001539679600002

EID výsledku v databázi Scopus

2-s2.0-105011158690