Association Rules in Parameter Tuning : for Experimental Designs

Sammanfattning: The objective of this thesis was to investigate the possibility ofusing association rule algorithms to automatically generaterules for the output of a Parameter Tuning framework. Therules would be the basis for a recommendation to the user regardingwhich parameter space to reduce during experimentation.The parameter tuning output was generated by means ofan open source project (INPUT) example program. InPUT is atool used to describe computer experiment configurations in aframework independent input/output format. InPUT has adaptersfor the evolutionary algorithm framework Watchmakerand the tuning framework SPOT. The output was imported in Rand preprocessed to a format suitable for association rule algorithms.Experiments were conducted on data for which theparameter spaces were discretized in 2, 5, 10 steps. The minimumsupport threshold was set to 1% and 3% to investigatethe amount of rules over time. The Apriori and Eclat algorithmsproduced exactly the same amount of rules, and the top 5rules with regards to support were basically the same for bothalgorithms. It was not possible at the time to automatically distinguishinguseful rules. In combination with the many manualdecisions during the process of converting the tuning output toassociation rules, the conclusion was reached to not recommendassociation rules for enhancing the Parameter Tuningprocess.