A preliminary study on learning challenges in machine learning-based flight delay prediction

Abstract

Machine learning based flight delay prediction is one of the numerous real-life application domains where the problem of imbalance in class distribution is reported to affect the performance of learning algorithms. However, the fact that learning algorithms have been reported to perform well on some class imbalance problems posits the possibility of other contributing factors. In this study, we visually explore air traffic data after dimensionality reduction with t-Distributed Stochastic Neighbour Embedding. Our initial findings suggest a high degree of overlapping between the delayed and on-time class instances which can be a greater problem for learning algorithms than class imbalance.