Szczegóły publikacji
Opis bibliograficzny
Evaluating the effectiveness and stability of the constrained hybrid metaheuristic algorithm in probabilistic neural networks training / Szymon KUCHARCZYK, Piotr A. KOWALSKI, Jacek MAŃDZIUK // W: Computational Science – ICCS 2026 : 26th International Conference : Hamburg, Germany, June 29–July 1, 2026 : proceedings , Pt. 2 / eds. Philipp Neumann [et al.]. — Cham : Springer, cop. 2026. — ( Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 16784 ). — ISBN: 978-3-032-29923-9; e-ISBN: 978-3-032-29924-6. — S. 558–566. — Bibliogr., Abstr. — Publikacja dostępna online od: 2026-06-27. — P. A. Kowalski - dod. afiliacja: Systems Research Institute, Polish Academy of Sciences, Warsaw. — J. Mańdziuk - dod. afiliacja: Warsaw University of Technology
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 168888 |
|---|---|
| Data dodania do BaDAP | 2026-08-28 |
| DOI | 10.1007/978-3-032-29924-6_52 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2026 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Probabilistic Neural Networks (PNNs) are memory-based networks that have been used successfully for classification and regression tasks. Training of PNNs may be performed by analytical methods, e.g., plug-in or by heuristic methods, e.g., Particle Swarm Optimization. Heuristic methods are superior to traditional PNN training techniques because of their nonparametric behavior, independence of the PNN kernel selection, and ability to optimize method parameters for a given problem. One of the recently proposed training algorithms - constrained Hybrid Metaheuristic (cHM), overperformed other analytical and heuristic procedures on a variety of datasets. Here, we present a further evaluation of the cHM method for training PNNs for classification tasks. In particular, we study its effectiveness and stability with different hyperparameters across 10 datasets. The results show that the cHM training procedure is generally stable and the parameter selection does not significantly impact the PNN training accuracy for 8 of 10 tested datasets.
