Abstract
We use the concept of conditional mutual information (MI) to approach problems involving the selection of variables in the area of medical diagnosis. Computing MI requires estimates of joint distributions over collections of variables. However, in general computing accurate joint distributions conditioned on a large set of variables is expensive in terms of data and computing power. Therefore, one must seek alternative ways to calculate the relevant quantities and still use all the available observations. We describe and compare a basic approach consisting of averaging MI estimates conditioned on individual observations and another approach where it is possible to condition on all observations at once by making some conditional independence assumptions. This yields a data-efficient variant of information maximization for test selection. We present experimental results on public heart disease data and data from a controlled study in the area of breast cancer diagnosis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley Interscience, Hoboken (1991)
Mueller, M., Rosales, R., Steck, H., Krishnan, S., Rao, B., Kramer, S.: Data-Efficient Information-Theoretic Test Selection, Technical Report TUM-I0910, Institut für Informatik, TU München (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mueller, M., Rosales, R., Steck, H., Krishnan, S., Rao, B., Kramer, S. (2009). Data-Efficient Information-Theoretic Test Selection. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds) Artificial Intelligence in Medicine. AIME 2009. Lecture Notes in Computer Science(), vol 5651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02976-9_58
Download citation
DOI: https://doi.org/10.1007/978-3-642-02976-9_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02975-2
Online ISBN: 978-3-642-02976-9
eBook Packages: Computer ScienceComputer Science (R0)