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  • Fusion of Classifiers based on a Novel 2-Stage Model

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    Accepted Manuscript (AM)
    Author(s)
    Nguyen, TT
    Liew, AWC
    Tran, MT
    Thu Thuy Nguyen, T
    Nguyen, MP
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2014
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    Abstract
    The paper introduces a novel 2-Stage model for multi-classifier system. Instead of gathering posterior probabilities resulted from base classifiers into Level1 data like in the original 2-Stage model, here we separate data in K Level1 matrices corresponding to the K base classifiers. These data matrices, in turn, are classified in sequence by a new classifier at the second stage to generate Level2 data. Next, Weight Matrix is proposed to combine Level2 data and predict label of observations in test set. Experimental results on CLEF2009 medical image database demonstrate the benefit of our model in comparison with several ...
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    The paper introduces a novel 2-Stage model for multi-classifier system. Instead of gathering posterior probabilities resulted from base classifiers into Level1 data like in the original 2-Stage model, here we separate data in K Level1 matrices corresponding to the K base classifiers. These data matrices, in turn, are classified in sequence by a new classifier at the second stage to generate Level2 data. Next, Weight Matrix is proposed to combine Level2 data and predict label of observations in test set. Experimental results on CLEF2009 medical image database demonstrate the benefit of our model in comparison with several ensemble learning models.
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    Conference Title
    Communications in Computer and Information Science
    Volume
    481
    Publisher URI
    http://www.icmlc.com/ICMLC/formerICMLC_2014.html
    DOI
    https://doi.org/10.1007/978-3-662-45652-1_7
    Copyright Statement
    © 2014 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
    Subject
    Expert Systems
    Publication URI
    http://hdl.handle.net/10072/66970
    Collection
    • Conference outputs

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