A Preliminary Study on Learning Challenges in Machine Learning-based Flight Delay Prediction

Mustapha, Ismail B.; Shamsuddin, Siti Mariyam; Hasan, Shafaatunnur

doi:10.11113/ijic.v9n1.204

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…What makes this problem even more interesting is that, in most cases, the minority classes are often the classes of interest. Several manifestations of this problem abound in many real-life application domains of ML like medical diagnosis [7,8], fraud detection [9][10][11], flight delay prediction [12,13] amongst others.…”

Section: Introductionmentioning

confidence: 99%

Investigating Group Distributionally Robust Optimization for Deep Imbalanced Learning: A Case Study of Binary Tabular Data Classification

Mustapha¹,

Hasan²,

Nabbus³

et al. 2023

IJACSA

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One of the most studied machine learning challenges that recent studies have shown the susceptibility of deep neural networks to is the class imbalance problem. While concerted research efforts in this direction have been notable in recent years, findings have shown that the canonical learning objective, empirical risk minimization (ERM), is unable to achieve optimal imbalance learning in deep neural networks given its bias to the majority class. An alternative learning objective, group distributionally robust optimization (gDRO), is investigated in this study for imbalance learning, focusing on tabular imbalanced data as against image data that has dominated deep imbalance learning research. Contrary to minimizing average per instance loss as in ERM, gDRO seeks to minimize the worst group loss over the training data. Experimental findings in comparison with ERM and classical imbalance methods using four popularly used evaluation metrics in imbalance learning across several benchmark imbalance binary tabular data of varying imbalance ratios reveal impressive performance of gDRO, outperforming other compared methods in terms of g-mean and roc-auc.

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Section: Introductionmentioning

confidence: 99%

Investigating Group Distributionally Robust Optimization for Deep Imbalanced Learning: A Case Study of Binary Tabular Data Classification

Mustapha¹,

Hasan²,

Nabbus³

et al. 2023

IJACSA

Self Cite

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Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods

Hatıpoğlu,

Tosun

2024

Applied Sciences

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Flight delays represent a significant challenge in the global aviation industry, resulting in substantial costs and a decline in passenger satisfaction. This study addresses the critical issue of predicting flight delays exceeding 15 min using machine learning techniques. The arrival delays at a Turkish airport are analyzed utilizing a novel dataset derived from airport operations. This research examines a range of machine learning models, including Logistic Regression, Naïve Bayes, Neural Networks, Random Forest, XGBoost, CatBoost, and LightGBM. To address the issue of imbalanced data, additional experiments are conducted using the Synthetic Minority Over-Sampling Technique (SMOTE), in conjunction with the incorporation of meteorological data. This multi-faceted approach ensures robust forecast performance under varying conditions. The SHAP (SHapley Additive exPlanations) method is employed to interpret the relative importance of features within the models. The study is based on a three-year period of flight data obtained from a Turkish airport. The dataset is sufficiently extensive and robust to provide a reliable foundation for analysis. The results indicate that XGBoost is the most proficient model for the dataset, demonstrating its potential to deliver highly accurate predictions with an accuracy of 80%. The impact of weather factors on the predictions is found to be insignificant in comparison to scenarios without weather data in this dataset.

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A Preliminary Study on Learning Challenges in Machine Learning-based Flight Delay Prediction

Cited by 2 publications

References 9 publications

Investigating Group Distributionally Robust Optimization for Deep Imbalanced Learning: A Case Study of Binary Tabular Data Classification

Investigating Group Distributionally Robust Optimization for Deep Imbalanced Learning: A Case Study of Binary Tabular Data Classification

Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods

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