Feature Selection for Meta-learning

This work discovered that the best set of discriminating attributes is different for every pair of inducers, and applied a feature selection method on the meta-learning problems, to get thebest set of attributes for each problem.

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 applied to meta-learning problems, each one associated with a specific pair of inducers. The generated models were used to provide a ranking of inducers on new datasets. Instance-based learning assumes that all the attributes have the same importance. We discovered that the best set of discriminating attributes is different for every pair of inducers.We applied a feature selection method on the meta-learning problems, to get the best set of attributes for each problem. The performance of the system is significantly improved.