Título: Interpretative Variable Selection via Perfect Bipartite Matching

Data: 29/08/2023

Horário: 16h00

Local: Sala de Seminários - Bloco 952

Resumo:

Feature selection is a fundamental process in machine learning to identify the most relevant subset of features for a given problem. Among the various feature selection approaches, filter methods stand out for their simplicity and efficiency. However, these methods lack interpretability regarding the relationships between the selected and unselected features. To address this challenge, we propose a novel pairwise feature selection method based on Perfect Bipartite Matching, which establishes optimized linear relationships between features, thus facilitating the interpretation of feature connections. Empirical evaluations using 18 UCI datasets demonstrate the effectiveness of our approach compared to baseline methods. Furthermore, we present a case study on Chagas disease, showcasing detailed interpretation results and the significance of selected features in sudden cardiac death prevention. The emphasis on interpretability contributes to developing more effective and transparent Computer-Aided Diagnosis (CAD) tools, facilitating early detection and prevention efforts in medical diagnostic applications.

Banca examinadora:

• Prof. Dr. João Paulo Pordeus Gomes (MDCC/UFC - Orientador)
• Prof. Dr. João Paulo do Vale Madeiro (UFC - Coorientador)
• Prof. Dr. José Maria da Silva Monteiro Filho (UFC)
• Prof. Dr. Roberto Coury Pedrosa (UFRJ)