Learning to Represent Codons: A Challenge Problem for Constructive Induction

The ability of an inductive learning system to nd a good solution to a given problem is dependent upon the representation used for the features of the problem. Systems that perform constructive induction are able to change their representation by constructing new features. We describe an important, real-world problem { nding genes in DNA { that we believe offers an interesting challenge to constructive-induction researchers. We report experiments that demonstrate that: (1) two diierent input representations for this task result in signii-cantly diierent generalization performance for both neural networks and decision trees; and (2) both neural and symbolic methods for constructive induction fail to bridge the gap between these two representations. We believe that this real-world domain provides an interesting challenge problem for constructive induction because the relationship between the two representations is well known, and because the representational shift involved in constructing the better representation is not imposing.