Advertisement

Parameter Screening and Optimisation for ILP Using Designed Experiments

  • Ashwin Srinivasan
  • Ganesh Ramakrishnan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5989)

Abstract

Reports of experiments conducted with an Inductive Logic Programming system rarely describe how specific values of parameters of the system are arrived at when constructing models. Usually, no attempt is made to identify sensitive parameters, and those that are used are often given “factory-supplied” default values, or values obtained from some non-systematic exploratory analysis. The immediate consequence of this is, of course, that it is not clear if better models could have been obtained if some form of parameter selection and optimisation had been performed. Questions follow inevitably on the experiments themselves: specifically, are all algorithms being treated fairly, and is the exploratory phase sufficiently well-defined to allow the experiments to be replicated? In this paper, we investigate the use of parameter selection and optimisation techniques grouped under the study of experimental design. Screening and “response surface” methods determine, in turn, sensitive parameters and good values for these parameters. This combined use of parameter selection and response surface-driven optimisation has a long history of application in industrial engineering, and its role in ILP is investigated using two well-known benchmarks. The results suggest that computational overheads from this preliminary phase are not substantial, and that much can be gained, both on improving system performance and on enabling controlled experimentation, by adopting well-established procedures such as the ones proposed here.

Keywords

Response Surface Parameter Selection Full Factorial Design Inductive Logic Programming Fractional Design 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bengio, Y.: Gradient based optimisation of hyperparameters. Neural Computation 12(8), 1889–1900 (2000)CrossRefGoogle Scholar
  2. 2.
    Bengio, Y., Grandvalet, Y.: No unbiased estimator of the variance of k-fold cross-validation. Journal of Machine Learning Research 5, 1089–1105 (2004)MathSciNetGoogle Scholar
  3. 3.
    Box, G.E.P., Wilson, K.B.: On the Experimental Attainment of Optimum Conditions. Journal of the Royal Statistical Society, Series B (Methodological) 13(1), 1–45 (1951)MathSciNetGoogle Scholar
  4. 4.
    Bratko, I., Muggleton, S.H.: Applications of Inductive Logic Programming. Communications of the ACM 38(11), 65–70 (1995)CrossRefGoogle Scholar
  5. 5.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, Belmont (1984)Google Scholar
  6. 6.
    Dzeroski, S.: Relational Data Mining Applications: An Overview. In: Dzeroski, S., Lavrac, N. (eds.) Relational Data Mining, pp. 339–360. Springer, Berlin (2001)Google Scholar
  7. 7.
    King, R.D., Muggleton, S.H., Srinivasan, A., Sternberg, M.J.E.: Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. Proc. of the National Academy of Sciences 93, 438–442 (1996)CrossRefGoogle Scholar
  8. 8.
    King, R.D., Srinivasan, A.: Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming. Environmental Health Perspectives 104(5), 1031–1040 (1996)CrossRefGoogle Scholar
  9. 9.
    Montgomery, D.C.: Design and Analysis of Experiments, 5th edn. John Wiley, New York (2005)zbMATHGoogle Scholar
  10. 10.
    Srinivasan, A.: Four Suggestions and a Rule Concerning the Application of ILP. In: Lavrac, N., Dzeroski, S. (eds.) Relational Data Mining, pp. 365–374. Springer, Berlin (2001)Google Scholar
  11. 11.
    Steppan, D.D., Werner, J., Yeater, R.P.: Essential Regression and Experimental Design for Chemists and Engineers (1998), http://www.jowerner.homepage.t-online.de/download.htm

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ashwin Srinivasan
    • 1
  • Ganesh Ramakrishnan
    • 2
  1. 1.IBM India Research LaboratoryNew DelhiIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of TechnologyBombayIndia

Personalised recommendations