A0337
Title: Interpretable cost-sensitive ensembling
Authors: Stefan Van Aelst - University of Leuven (Belgium) [presenting]
Tim Verdonck - KU Leuven and UAntwerpen - imec (Belgium)
Bing Yang - KULeuven (Belgium)
Abstract: In many applications, the cost of wrong decisions is not symmetric. Therefore, it makes sense to take the costs associated with wrong decisions into account in the decision process to minimize the risks for stakeholders. To achieve this, cost-sensitive methods have been developed, such as cost-sensitive logistic regression. Moreover, for high-dimensional data, ensemble models often yield a much better performance than a single (sparse) model. However, ensembles of a large number of models are difficult to interpret. A split-learning framework has been recently developed to combine the interpretability of a single model with the performance of ensemble models. This framework is used to introduce a diverse ensemble of cost-sensitive logistic regression models. This yields an ensemble that is interpretable with a low misclassification cost. To solve the non-convex optimization problem, a novel algorithm based on the partial conservative convex separable quadratic approximation is developed. The proposed method delivers outstanding savings, as demonstrated through extensive simulation and real-world applications in fraud detection and gene expression analysis.
