MODEL SELECTION VIA META-LEARNING: A COMPARATIVE STUDY

Abstract

The selection of an appropriate inducer is crucial for performing effective classification. In previous work we presented a system called NOEMON which relied on a mapping between dataset characteristics and inducer performance to propose inducers for specific datasets. Instance-based learning was used to create that mapping. Here we explore the use of decision trees inducers as the inducers on the meta-learning level. We believe that they posses a set of properties that match the properties of the meta-learning problem that we are trying to solve. The results show that the performance of the system is indeed improved with the use of the decision tree learners.