Single and combined algorithms for open set classification on image datasets
Generally, classification models have closed nature, and they are constrained by the number of classes in the training data. Hence, classifying "unknown" - OOD (out-of-distribution) - samples is challenging, especially in the so called "open set" problem. We propose and investiga...
Elmentve itt :
| Szerzők: | |
|---|---|
| Dokumentumtípus: | Cikk |
| Megjelent: |
University of Szeged, Institute of Informatics
Szeged
2024
|
| Sorozat: | Acta cybernetica
26 No. 3 |
| Kulcsszavak: | Bináris osztályozás, Többosztályos osztályozás, Adattudomány, Algoritmus |
| Tárgyszavak: | |
| doi: | 10.14232/actacyb.298356 |
| Online Access: | http://acta.bibl.u-szeged.hu/86976 |
| Tartalmi kivonat: | Generally, classification models have closed nature, and they are constrained by the number of classes in the training data. Hence, classifying "unknown" - OOD (out-of-distribution) - samples is challenging, especially in the so called "open set" problem. We propose and investigate different solutions - single and combined algorithms - to tackle this task, where we use and expand a K-classifier to be able to identify K+1 classes. They do not require any retraining or modification on the K-classifier architecture. We show their strengths when avoiding type I or type II errors is fundamental. We also present a mathematical representation for the task to estimate the K+1 classification accuracy, and an inequality that defines its boundaries. Additionally, we introduce a formula to calculate the exact K+1 classification accuracy. |
|---|---|
| Terjedelem/Fizikai jellemzők: | 297-322 |
| ISSN: | 2676-993X |