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Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling

  • Houssam Nassif
  • Finn Kuusisto
  • Elizabeth S. Burnside
  • David Page
  • Jude Shavlik
  • Vítor Santos Costa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8190)

Abstract

We introduce Score As You Lift (SAYL), a novel Statistical Relational Learning (SRL) algorithm, and apply it to an important task in the diagnosis of breast cancer. SAYL combines SRL with the marketing concept of uplift modeling, uses the area under the uplift curve to direct clause construction and final theory evaluation, integrates rule learning and probability assignment, and conditions the addition of each new theory rule to existing ones.

Breast cancer, the most common type of cancer among women, is categorized into two subtypes: an earlier in situ stage where cancer cells are still confined, and a subsequent invasive stage. Currently older women with in situ cancer are treated to prevent cancer progression, regardless of the fact that treatment may generate undesirable side-effects, and the woman may die of other causes. Younger women tend to have more aggressive cancers, while older women tend to have more indolent tumors. Therefore older women whose in situ tumors show significant dissimilarity with in situ cancer in younger women are less likely to progress, and can thus be considered for watchful waiting.

Motivated by this important problem, this work makes two main contributions. First, we present the first multi-relational uplift modeling system, and introduce, implement and evaluate a novel method to guide search in an SRL framework. Second, we compare our algorithm to previous approaches, and demonstrate that the system can indeed obtain differential rules of interest to an expert on real data, while significantly improving the data uplift.

Keywords

Watchful Waiting Inductive Logic Programming Theory Rule Lift Curve Mammography Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Houssam Nassif
    • 1
  • Finn Kuusisto
    • 1
  • Elizabeth S. Burnside
    • 1
  • David Page
    • 1
  • Jude Shavlik
    • 1
  • Vítor Santos Costa
    • 1
    • 2
  1. 1.University of WisconsinMadisonUSA
  2. 2.CRACS-INESC TEC and DCC-FCUPUniversity of PortoPortugal

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