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)


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.


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.


  1. 1.
    American Cancer Society: Breast Cancer Facts & Figures 2009-2010. American Cancer Society, Atlanta, USA (2009)Google Scholar
  2. 2.
    American Cancer Society: Cancer Facts & Figures 2009. American Cancer Society, Atlanta, USA (2009)Google Scholar
  3. 3.
    American College of Radiology, Reston, VA, USA: Breast Imaging Reporting and Data System (BI-RADSTM), 3rd edn. (1998)Google Scholar
  4. 4.
    Blockeel, H., De Raedt, L.: Top-down induction of first-order logical decision trees. Artificial Intelligence 101, 285–297 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Boyd, K., Davis, J., Page, D., Santos Costa, V.: Unachievable region in precision-recall space and its effect on empirical evaluation. In: Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland (2012)Google Scholar
  6. 6.
    Cleary, T.A.: Test bias: Prediction of grades of negro and white students in integrated colleges. Journal of Educational Measurement 5(2), 115–124 (1968)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Davis, J., Burnside, E., de Castro Dutra, I., Page, D.L., Santos Costa, V.: An integrated approach to learning bayesian networks of rules. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 84–95. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Fowble, B.L., Schultz, D.J., Overmoyer, B., Solin, L.J., Fox, K., Jardines, L., Orel, S., Glick, J.H.: The influence of young age on outcome in early stage breast cancer. Int. J. Radiat. Oncol. Biol. Phys. 30(1), 23–33 (1994)CrossRefGoogle Scholar
  9. 9.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)CrossRefzbMATHGoogle Scholar
  10. 10.
    Getoor, L., Taskar, B. (eds.): An Introduction to Statistical Relational Learning. MIT Press (2007)Google Scholar
  11. 11.
    Hansotia, B., Rukstales, B.: Incremental value modeling. Journal of Interactive Marketing 16(3), 35–46 (2002)CrossRefGoogle Scholar
  12. 12.
    Jaśkowski, M., Jaroszewicz, S.: Uplift modeling for clinical trial data. In: ICML 2012 Workshop on Clinical Data Analysis, Edinburgh, Scotland (2012)Google Scholar
  13. 13.
    Jayasinghe, U.W., Taylor, R., Boyages, J.: Is age at diagnosis an independent prognostic factor for survival following breast cancer? ANZ J. Surg. 75(9), 762–767 (2005)CrossRefGoogle Scholar
  14. 14.
    Lavrac, N., Dzeroski, S.: Inductive Logic Programming: Techniques and Applications, Ellis Horwood, New York (1994)Google Scholar
  15. 15.
    Linn, R.L.: Single-group validity, differential validity, and differential prediction. Journal of Applied Psychology 63, 507–512 (1978)CrossRefGoogle Scholar
  16. 16.
    Lo, V.S.: The true lift model - a novel data mining approach to response modeling in database marketing. SIGKDD Explorations 4(2), 78–86 (2002)CrossRefGoogle Scholar
  17. 17.
    Muggleton, S.H.: Inverse entailment and Progol. New Generation Computing 13, 245–286 (1995)CrossRefGoogle Scholar
  18. 18.
    Nassif, H., Page, D., Ayvaci, M., Shavlik, J., Burnside, E.S.: Uncovering age-specific invasive and DCIS breast cancer rules using Inductive Logic Programming. In: ACM International Health Informatics Symposium (IHI), Arlington, VA, pp. 76–82 (2010)Google Scholar
  19. 19.
    Nassif, H., Woods, R., Burnside, E.S., Ayvaci, M., Shavlik, J., Page, D.: Information extraction for clinical data mining: A mammography case study. In: IEEE International Conference on Data Mining (ICDM) Workshops, Miami, Florida, pp. 37–42 (2009)Google Scholar
  20. 20.
    Nassif, H., Santos Costa, V., Burnside, E.S., Page, D.: Relational differential prediction. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012, Part I. LNCS, vol. 7523, pp. 617–632. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    Nassif, H., Wu, Y., Page, D., Burnside, E.S.: Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women. In: American Medical Informatics Association Symposium (AMIA), Chicago, pp. 1330–1339 (2012)Google Scholar
  22. 22.
    Page, D., Santos Costa, V., Natarajan, S., Barnard, A., Peissig, P., Caldwell, M.: Identifying adverse drug events by relational learning. In: AAAI 2012, Toronto, pp. 1599–1605 (2012)Google Scholar
  23. 23.
    Radcliffe, N.J., Surry, P.D.: Differential response analysis: Modeling true response by isolating the effect of a single action. In: Credit Scoring and Credit Control VI, Edinburgh, Scotland (1999)Google Scholar
  24. 24.
    Radcliffe, N.J., Surry, P.D.: Real-world uplift modelling with significance-based uplift trees. White Paper TR-2011-1, Stochastic Solutions (2011)Google Scholar
  25. 25.
    Rzepakowski, P., Jaroszewicz, S.: Decision trees for uplift modeling with single and multiple treatments. Knowledge and Information Systems 32, 303–327 (2012)CrossRefGoogle Scholar
  26. 26.
    Schnitt, S.J.: Local outcomes in ductal carcinoma in situ based on patient and tumor characteristics. J. Natl. Cancer Inst. Monogr. 2010(41), 158–161 (2010)CrossRefGoogle Scholar
  27. 27.
    Srinivasan, A.: The Aleph Manual, 4th edn. (2007),
  28. 28.
    Tabar, L., Tony Chen, H.H., Amy Yen, M.F., Tot, T., Tung, T.H., Chen, L.S., Chiu, Y.H., Duffy, S.W., Smith, R.A.: Mammographic tumor features can predict long-term outcomes reliably in women with 1-14-mm invasive breast carcinoma. Cancer 101(8), 1745–1759 (2004)CrossRefGoogle Scholar
  29. 29.
    Thurfjell, M.G., Lindgren, A., Thurfjell, E.: Nonpalpable breast cancer: Mammographic appearance as predictor of histologic type. Radiology 222(1), 165–170 (2002)CrossRefGoogle Scholar
  30. 30.
    Tufféry, S.: Data Mining and Statistics for Decision Making, 2nd edn. John Wiley & Sons (2011)Google Scholar
  31. 31.
    Young, J.W.: Differential validity, differential prediction, and college admissions testing: A comprehensive review and analysis. Research Report 2001-6, The College Board, New York (2001)Google Scholar
  32. 32.
    Zelezný, F., Lavrac, N.: Propositionalization-based relational subgroup discovery with RSD. Machine Learning 62(1-2), 33–66 (2006)CrossRefGoogle Scholar

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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