Explainable profit-driven hotel booking cancellation prediction based on heterogeneous stacking-based ensemble classification

• Hotel booking cancellation prediction (HBCP) serves capacity and retention management. • IPHSEC is a novel ensemble classifier for profit-driven HBCP. • Our framework focuses on profitability and explainability. • IPHSEC outperforms other models in profit and reveals valuable insights. The goal of hotel booking cancellation prediction in the hospitality industry is to identify potential cancellations from a large customer base and improve the efficiency of customer retention and capacity management efforts. Whilst prior research has shown that the predictive performance of hotel booking cancellation prediction can be further enhanced by integrating multiple classifiers, the explainability of such models is limited due to low interpretability and limited alignment with company goals. To address this limitation, we propose a novel heterogeneous linear stacking ensemble classifier for profit-driven hotel booking cancellation prediction. It enhances classifier explainability by (1) making models more accountable by axing model training towards profitability and (2) complementing models by global post-hoc model interpretation strategies. Through experiments based on real-world datasets, our proposed classification framework is demonstrated to lead to greater profits than other profit-oriented predictive models. Moreover, an in-depth interpretability analysis demonstrates the framework's ability to identify critical factors significantly impacting hotel cancellations, providing valuable insights for retention campaigns.