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Pessimistic Uplift Modeling

Atef Shaar,T. Abdessalem,Olivier Segard

Published 2016 in Knowledge Discovery and Data Mining

ABSTRACT

Uplift modeling is a machine learning technique that aims to model treatment effects heterogeneity. It has been used in business and health sectors to predict the effect of a specific action on a given individual. Despite its advantages, uplift models show high sensitivity to noise and disturbance, which leads to unreliable results. In this paper we show different approaches to address the problem of uplift modeling, we demonstrate how disturbance in data can affect uplift measurement. We propose a new approach, we call it Pessimistic Uplift Modeling, that minimizes disturbance effects. We compared our approach with the existing uplift methods, on simulated and real data-sets. The experiments show that our approach outperforms the existing approaches, especially in the case of high noise data environment.

PUBLICATION RECORD

  • Publication year

    2016

  • Venue

    Knowledge Discovery and Data Mining

  • Publication date

    2016-03-31

  • Fields of study

    Medicine, Computer Science, Mathematics

  • Identifiers
    arXiv 1603.09738
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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