OACE: Multi-criteria Assessment of Assertiveness and Cost for Deep Learning Models - Pinto, Lyanh Vinicios Lopes; Seruffo, Marcos Cesar da Rocha (Anais Estendidos do Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia); 2025: Anais Estendidos do XXXI Simpósio Brasileiro de Sistemas Multimídia e WebDO - 10.5753/webmedia_estendido.2025.15959, 2025)
The rapid expansion of Deep Learning (DL) has intensified the need for more rigorous evaluation methodologies for implementing solutions in real-world scenarios. In view of this, this work presents Optimized Assertiveness-Cost Evaluation (OACE), a method based on Multi-Criteria Decision Making that integrates assertiveness and computational cost criteria into a single parameterizable function, aiming to systematize the evaluation of DL models. To demonstrate its effectiveness, an experiment was conducted with Random Walk and the CIFAR-10 database, evaluating five DL architectures in a balanced scenario. The findings identified MobileNetV2 as the best model for the defined scenario, with a score of 𝑆𝜙 (𝑚) = 0.9541, surpassing MobileNetB0 by 38%, due to its superior efficiency.