Comparison of Loss Functions in the KarciFANN Method

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dc.contributor.authorSaygili, Hulya
dc.contributor.authorKarakurt, Meral
dc.contributor.authorKarci, Ali
dc.date.accessioned2026-04-04T13:18:59Z
dc.date.available2026-04-04T13:18:59Z
dc.date.issued2024
dc.departmentİnönü Üniversitesi
dc.description8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- 21 September 2024 through 22 September 2024 -- Malatya -- 203423
dc.description.abstractVarious hyperparameters are used in the design phase of artificial neural networks (ANNs). One of the hyperparameters that significantly affects the performance of ANNs is the optimization method. In this paper, the Karci Fractional-Order Artificial Neural Network (KarciFANN) method is proposed, where learning is performed using fractional-order derivatives instead of a fixed learning rate parameter used in the SGD optimization method. Thus, in the KarciFANN method, an external intervention to the network is minimized by using a fractional-order derivative that changes according to the value of the loss function obtained at each iteration. The loss function is another hyperparameter that affects the performance of ANNs. It is a mathematical function that measures the difference between the expected true value and the predicted value produced by the network. The loss function indicates how effectively the ANN has learned. In backpropagation-based ANNs, the value of the loss function is propagated backward from the network output to the input layer to update the weights, aiming to minimize this loss value and improve the network's performance. In this study, the performance of the mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) loss functions was compared using the KarciFANN method for classifying the Gina_Prior2 dataset. As a result of the experiments performed on the designed model, it was seen that the most successful loss function was MSE, which produced accuracy values over 99%. © 2024 IEEE.
dc.identifier.doi10.1109/IDAP64064.2024.10710967
dc.identifier.isbn979-833153149-2
dc.identifier.scopus2-s2.0-85207948131
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP64064.2024.10710967
dc.identifier.urihttps://hdl.handle.net/11616/108058
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250329
dc.subjectartificial neural networks
dc.subjectKarciFANN
dc.subjectloss functions
dc.subjectmae
dc.subjectmse
dc.subjectrmse
dc.titleComparison of Loss Functions in the KarciFANN Method
dc.title.alternativeKarciFANN Y nteminde Kayip Fonksiyonlarinin Karsilastirilmasi]
dc.typeConference Object

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