Learning Discriminative Distance Functions for Case Retrieval and Decision Support

The importance of learning distance functions is gradually being acknowledged by the machine learning community, and different techniques are suggested that can successfully learn a strong distance function in many various contexts. Nevertheless the studies in the area are still rather fragmentary; they lack systematic analysis and focus on a limited circle of application domains. In this paper, two techniques for learning discriminative distance function are evaluated and compared on biomedical data of different kind; learning from equivalence constraints and the intrinsic Random Forest similarity. Both techniques demonstrate competitive results with respect to plain learning; the Random Forest similarity exhibits a more robust behaviour and is shown to be less susceptible to missing data and noise.