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001-es BibID:	BIBFORM116613
035-os BibID:	(cikkazonosító)4730 (Scopus)85178934416
Első szerző:	Harangi Balázs (programtervező matematikus)
Cím:	Composing Diverse Ensembles of Convolutional Neural Networks by Penalization / Balazs Harangi, Agnes Baran, Marcell Beregi-Kovacs, Andras Hajdu
Dátum:	2023
ISSN:	2227-7390
Megjegyzések:	Ensemble-based systems are well known to have the capacity to outperform individual approaches if the ensemble members are sufficiently accurate and diverse. This paper investigates how an efficient ensemble of deep convolutional neural networks (CNNs) can be created by forcing them to adjust their parameters during the training process to increase diversity in their decisions. As a new theoretical approach to reach this aim, we join the member neural architectures via a fully connected layer and insert a new correlation penalty term in the loss function to obstruct their similar operation. With this complementary term, we implement the standard guideline of ensemble creation to increase the members` diversity for CNNs in a more detailed and flexible way than similar existing techniques. As for applicability, we show that our approach can be efficiently used in various classification tasks. More specifically, we demonstrate its performance in challenging medical image analysis and natural image classification problems. Besides the theoretical considerations and foundations, our experimental findings suggest that the proposed technique is competitive. Namely, on the one hand, the classification rate of the ensemble trained in this way outperformed all the individual accuracies of the state-of-the-art member CNNs according to the standard error functions of these application domains. On the other hand, it is also validated that the ensemble members get more diverse and their accuracies are raised by adding the penalization term. Moreover, we performed a full comparative analysis, including other state-of-the-art ensemble-based approaches recommended for the same classification tasks. This comparative study also confirmed the superiority of our method, as it overcame the current solutions.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk ensemble-based network penalization loss function image classification diversity
Megjelenés:	Mathematics. - 11 : 23 (2023), p. 1-19. -
További szerzők:	Baran Ágnes (1972-) (matematikus) Beregi-Kovács Marcell (1995-) (Alkalmazott matematikus) Hajdu András (1973-) (matematikus, informatikus)
Pályázati támogatás:	ÚNKP-21-5-DE-485 Egyéb Bolyai János Kutatási Ösztöndíj MTA
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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