Co-evolutionary genetic programming for dataset similarity induction

Jakub Smíd, Martin Pilát, Klára Pesková, Roman Neruda

Abstract: Metalearning deals with an important problem in machine-learning, namely selecting the right techniques to model the data at hand. In most of the metalearning approaches, a notion of similarity between datasets is needed. Our approach derives the similarity measure by combining arbitrary attribute similarity functions ordered by the optimal attribute assignment. In this paper, we propose a genetic programming based approach to the evolution of an attribute similarity inducing function. The function is composed of two parts - one describes the similarity of categorical attributes, the other describes the similarity of numerical attributes. Co-evolution is used to put these two parts together to form the similarity function. We use a repairing approach to guarantee some of the metric features for this function, and also discuss which of these features are important in metalearning.