Application of Diversified Ensemble Learning in Real-life Business Problems: The Case of Predicting Costs of Forwarding Contracts - Trajanoska, Milena; Gjorgovski, Pavel; Zdravevski, Eftim (IEEE, 2022)
Finding an optimal machine learning model that can be applied to a business problem is a complex challenge that needs to provide a balance between multiple requirements, including a high predictive performance of the model, continuous learning and deployment, and explainability of the predictions. The topic of the FedCSIS 2022 Challenge: ‘Predicting the Costs of Forwarding Contracts’ is related to the challenges logistics and transportation companies are facing. To tackle these challenges, we established an entire Machine Learning framework which includes domain-specific feature engineering and enrichment, generic feature transformation and extraction, model hyperparameter tuning, and creating ensembles of traditional and deep learning models. Our contributions additionally include an analysis of the types of models which are suitable for the case of predicting a multi-modal continuous target variable, as well as an explainable analysis of the features which have the largest impact on predicting the value of these costs. We further show that ensembles created by combining multiple different models trained with different algorithms can improve the performance on unseen data. In this particular dataset, the experiments showed that such a combination improves the score by 3% compared to the best performing individual model.
Keywords: Costs of Forwarding Contract, explainability, prediction ensembles, Diversified Ensemble Learning