| Texto completo | |
| Autor(es): |
Zielinski, Kallil M. C.
;
Scabini, Leonardo
;
Ribas, Lucas C.
;
Bruno, Odemir M.
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2024 14TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION SYSTEMS, ICPRS; v. N/A, p. 7-pg., 2024-01-01. |
| Resumo | |
Network classification has become increasingly significant in understanding complex systems across various scientific fields. Life-like Network Automata (LLNA) has emerged as a powerful method for capturing the dynamic behavior of networks through Time-Evolution Patterns (TEPs). Despite LLNA's efficiency, the current method relies on a vast rule space, particularly with a Moore neighborhood of size 8, presenting a computational challenge and requiring a more efficient approach to rule selection without compromising classification accuracy. This paper aims to investigate the influence of varying neighborhood sizes on the performance of the LLNA-DTEP method and to assess the feasibility of reducing the computational load while maintaining high classification accuracy. An exhaustive search of all possible LLNA rules was conducted for different neighborhood ranges from 1 to 8 (Moore's neighborhood). For each rule, a feature vector was built based on histograms from the TEPs, which then was used in a Support Vector Machine (SVM) classifier to determine classification efficiency. The findings revealed that a reduced neighborhood range could significantly decrease the rule space and computational time. However, the impact on classification accuracy varied across four different datasets, with some showing robustness to changes in neighborhood size and others exhibiting notable sensitivity. The study shows that while reducing the neighborhood range in LLNA significantly reduces computational requirements, the choice of neighborhood size is a critical factor that must be tuned to each dataset's specific characteristics. (AU) | |
| Processo FAPESP: | 23/04583-2 - Reconhecimento de padrões em imagens baseado em redes neurais artificiais e sistemas complexos: da extração de descritores manuais ao aprendizado automático |
| Beneficiário: | Lucas Correia Ribas |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 23/10442-2 - Aprendizado profundo para reconhecimento de padrões em dados multissensores e multidimensionais |
| Beneficiário: | Leonardo Felipe dos Santos Scabini |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| Processo FAPESP: | 21/08325-2 - Análise de autômato de rede (network automata) como modelo para processos naturais e biológicos |
| Beneficiário: | Odemir Martinez Bruno |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 24/00530-4 - Agregação e aprendizado de características de textura com Vision Transformers e suas aplicações em imagens biológicas e médicas |
| Beneficiário: | Leonardo Felipe dos Santos Scabini |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado |
| Processo FAPESP: | 18/22214-6 - Rumo à convergência de tecnologias: de sensores e biossensores à visualização de informação e aprendizado de máquina para análise de dados em diagnóstico clínico |
| Beneficiário: | Osvaldo Novais de Oliveira Junior |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |