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Relational Differential Prediction

  • Houssam Nassif
  • Vítor Santos Costa
  • Elizabeth S. Burnside
  • David Page
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7523)

Abstract

A typical classification problem involves building a model to correctly segregate instances of two or more classes. Such a model exhibits differential prediction with respect to given data subsets when its performance is significantly different over these subsets. Driven by a mammography application, we aim at learning rules that predict breast cancer stage while maximizing differential prediction over age-stratified data. In this work, we present the first multi-relational differential prediction (aka uplift modeling) system, and propose three different approaches to learn differential predictive rules within the Inductive Logic Programming framework. We first test and validate our methods on synthetic data, then apply them on a mammography dataset for breast cancer stage differential prediction rule discovery. We mine a novel rule linking calcification to in situ breast cancer in older women.

Keywords

Uplift modeling relational data mining differential prediction inductive logic programming ILP stratified data breast cancer in situ 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Houssam Nassif
    • 1
  • Vítor Santos Costa
    • 2
  • Elizabeth S. Burnside
    • 1
  • David Page
    • 1
  1. 1.University of WisconsinMadisonUSA
  2. 2.University of PortoPortugal

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