Parameter screening and optimisation for ilp using designed experiments

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. Screening is done here by constructing a stepwise regression model relating the utility of an ILP system’s hypothesis to its input parameters, using systematic combinations of values of input parameters (technically speaking, we use a two-level fractional factorial design of the input parameters). The parameters use