Efficient Case Based Feature Construction

Feature construction is essential for solving many complex learning problems. Unfortunately, the construction of features usually implies searching a very large space of possibilities and is often computationally demanding. In this work, we propose a case based approach to feature construction. Learning tasks are stored together with a corresponding set of constructed features in a case base and can be retrieved to speed up feature construction for new tasks. The essential part of our method is a new representation model for learning tasks and a corresponding distance measure. Learning tasks are compared using relevance weights on a common set of base features only. Therefore, the case base can be built and queried very efficiently. In this respect, our approach is unique and enables us to apply case based feature construction not only on a large scale, but also in distributed learning scenarios in which communication costs play an important role. We derive a distance measure for heterogeneous learning tasks by stating a set of necessary conditions. Although the conditions are quite basic, they constraint the set of applicable methods to a surprisingly small number.