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  • A novel combining classifier method based on Variational Inference

    Author(s)
    Tien, Thanh Nguyen
    Thi, Thu Thuy Nguyen
    Xuan, Cuong Pham
    Liew, Alan Wee-Chung
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2016
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    Abstract
    In this paper, we propose a combining classifier method based on the Bayesian inference framework. Specifically, the outputs of base classifiers (called Level1 data or meta-data) are utilized in a combiner to produce the final classification. In our ensemble system, each class in the training set induces a distribution on the Level1 data, which is modeled by a multivariate Gaussian distribution. Traditionally, the parameters of the Gaussian are estimated using a maximum likelihood approach. However, maximum likelihood estimation cannot be applied since the covariance matrix of Level1 data of each class is not full rank. ...
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    In this paper, we propose a combining classifier method based on the Bayesian inference framework. Specifically, the outputs of base classifiers (called Level1 data or meta-data) are utilized in a combiner to produce the final classification. In our ensemble system, each class in the training set induces a distribution on the Level1 data, which is modeled by a multivariate Gaussian distribution. Traditionally, the parameters of the Gaussian are estimated using a maximum likelihood approach. However, maximum likelihood estimation cannot be applied since the covariance matrix of Level1 data of each class is not full rank. Instead, we propose to estimate the multivariate Gaussian distribution of Level1 data of each class by using the Variational Inference method. Experiments conducted on eighteen UCI Machine Learning Repository datasets and a selected 10-class CLEF2009 medical imaging database demonstrated the advantage of our method compared with several well-known ensemble methods.
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    Journal Title
    Pattern Recognition
    Volume
    49
    DOI
    https://doi.org/10.1016/j.patcog.2015.06.016
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/142514
    Collection
    • Journal articles

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