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 extend and refine the set of data characteristics; we also use a wider range of base-level inducers and a much larger collection of datasets to create the meta-models. We compare the performance of meta-models produced by instance based learners, decision trees and boosted decision trees. The results show that decision trees and boosted decision trees models enhance the perfomance of the system.