Transactions on Case-Based Reasoning

Volume 3 - Number 1 - October 2010 - Pages 1-16

Learning Discriminative Distance Functions for Case Retrieval and Decision Support

A. Tsymbal1, M. Huber1 and S.K. Zhou 2

1 Corporate Technology Div., Siemens AG, Erlangen, Germany
2 Siemens Corporate Research, Princeton NJ, USA


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.

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