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    The Bayesian decision tree technique using an adaptive sampling scheme

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    Authors
    Schetinin, Vitaly
    Krzanowski, Wojtek
    Maple, Carsten
    Issue Date
    2007
    Subjects
    Bayesian methods
    
    Metadata
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    Abstract
    Decision trees (DTs) provide an attractive classification scheme because clinicians responsible for making reliable decisions can easily interpret them. Bayesian averaging over DTs allows clinicians to evaluate the class posterior distribution and therefore to estimate the risk of making misleading decisions. The use of Markov chain Monte Carlo (MCMC) methodology of stochastic sampling makes the Bayesian DT technique feasible to perform. The Reversible Jump (RJ) extension of MCMC allows sampling from DTs of different sizes. However, the RJ MCMC process may become stuck in a particular DT far away from the region with maximal posterior. This negative effect can be mitigated by averaging the DTs obtained in different starts. In this paper we describe a new approach based on an adaptive sampling scheme. The performances of Bayesian DT techniques with the restarting and adaptive strategies are compared on a synthetic dataset as well as on some medical datasets. By quantitatively evaluating the classification uncertainty, we found that the adaptive strategy is superior to the restarting strategy.
    Citation
    Schetinin, V., Krzanowski, W. J., and Maple, C.,(2007) The Bayesian Decision Tree Technique Using an Adaptive Sampling Scheme, The 20th IEEE International Symposium on Computer-Based Medical Systems, CBMS-2007, Maribor, pp. 121-126
    Publisher
    IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
    URI
    http://hdl.handle.net/10547/270574
    DOI
    10.1109/CBMS.2007.109
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4262637
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISSN
    1063-7125
    ae974a485f413a2113503eed53cd6c53
    10.1109/CBMS.2007.109
    Scopus Count
    Collections
    Centre for Research in Distributed Technologies (CREDIT)

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