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A predict-and-optimize approach to profit-driven churn prevention
In this paper, we introduce a novel predict-and-optimize method for profit-driven churn prevention.
Efficient Hybrid Oversampling and Intelligent Undersampling for Imbalanced Big Data Classification
Imbalanced classification is a well-known challenge faced by many real-world applications.
OWAdapt: An adaptive loss function for deep learning using OWA operators
In this paper, we propose a fuzzy adaptive loss function for enhancing deep learning performance in classification tasks.
The foundations of cost-sensitive causal classification
The framework is shown to instantiate to application-specific cost-sensitive performance measures that have been recently proposed for evaluating customer retention and response uplift models, and allows to maximize profitability when adopting a causal classification model for optimizing decision-making.
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