Ant Colony Optimization Architecture for the CASH Problem in the Context of Machine Learning Classification - Fernandes, Diego D.; Silva, Thalyson G. N. da; Campos, Gustavo A. L. de; Barros, Ana Luiza B. de Paula; Marajo, Felipe G. (Anais do Congresso Latino-Americano de Software Livre e Tecnologias Abertas (Latinoware), 2025)
This paper presents ACOMV-CASH, an automated machine learning (AutoML) approach based on Ant Colony Optimization (ACO) to select a classifier from K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Decision Tree, Random Forest Classifier (RFC), and Support Vector Machine (SVM), while also optimizing model-specific hyperparameters. The framework incorporates a preprocessing pipeline that automatically prepares the dataset. Finally, the proposed algorithm brings the traditional ACO architecture back into the spotlight by structuring the search space as a directed graph (digraph) and incorporating novel strategies, such as the generation of continuous variables and hierarchical modeling. Evaluation of eight UCI datasets demonstrates competitive performance compared to existing AutoML frameworks, highlighting the robustness of ACOMV-CASH.