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A Non-Parametric Bayesian Approach for Uplift Discretization and Feature Selection

Mina Rafla 1, 2 Nicolas Voisine 1 Bruno Crémilleux 2 Marc Boullé 1 
2 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen
Abstract : Uplift modeling aims to estimate the incremental impact of a treatment, such as a marketing campaign or a drug, on an individual's outcome. Bank or Telecom uplift data often have hundreds to thousands of features. In such situations, detection of irrelevant features is an essential step to reduce computational time and increase model performance. We present a parameter-free feature selection method for uplift modeling founded on a Bayesian approach. We design an automatic feature discretization method for uplift based on a space of discretization models and a prior distribution. From this model space, we define a Bayes optimal evaluation criterion of a discretization model for uplift. We then propose an optimization algorithm that finds near-optimal discretization for estimating uplift in O(n log n) time. Experiments demonstrate the high performances obtained by this new discretization method. Then we describe a parameter-free feature selection method for uplift. Experiments show that the new method both removes irrelevant features and achieves better performances than state of the art methods.
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Contributor : Mina RAFLA Connect in order to contact the contributor
Submitted on : Tuesday, September 27, 2022 - 10:30:22 AM
Last modification on : Saturday, October 1, 2022 - 3:34:26 AM


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  • HAL Id : hal-03788912, version 1


Mina Rafla, Nicolas Voisine, Bruno Crémilleux, Marc Boullé. A Non-Parametric Bayesian Approach for Uplift Discretization and Feature Selection. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2022, Grenoble, France. ⟨hal-03788912⟩



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