Akademik Veri Yönetim Sistemi
nternational Symposium on the Analytic Hierarchy Process, Pennsylvania, Amerika Birleşik Devletleri, 13 - 15 Aralık 2024, cilt.1, sa.1, ss.1, (Tam Metin Bildiri)
Selecting suitable air traffic control (ATCO) candidates is critical due to the demanding responsibilities of the role, which requires rapid decision-making and strong stress management. Traditional methods, such as multistage interviews, are commonly used in this selection process but face challenges, including subjectivity and lack of standardization. To address these issues, we explore machine learning (ML) as an alternative to streamline and improve the reliability of candidate evaluations. This study develops three ML models including Logistic Regression (LR), Support Vector Machine SVM, and Decision Tree (DT) to replicate the outcomes of the interview phase. The candidate selection process was framed as a binary classification problem based on features such as previous exam results, high school background, and high school Grade Point Average. The results from the best-performing ML model are compared with previous studies that used AHP/ANP-based approaches. Our findings indicate that ML models can closely replicate interview exams, achieving accuracy rates of 95% for LR, 93% for DT, and 95% for SVM, highlighting their potential for practical application in ATCO candidate selection.