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Hierarchical Bayesian Classifier Combination

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10934))

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Abstract

This paper proposes a Bayesian method for combining the output of multiple base classifiers. The focus is put on combination methods for merging the outputs of several and possibly heterogeneous classifiers with the aim of gaining in the final accuracy. Our work is based on the Dawid and Skene’s work [11] for modelling disagreement among human assessors. We also take advantage of the Bayesian Model Averaging (BMA) framework without requiring the ensemble of base classifiers to correspond in a mutually exclusive and exhaustive way to all the possible data generating models. This makes our method relevant for combining multiple classifiers’ output each observing and predicting the behavior of an entity by means of divers aspects of the underlying environment. The proposed method, called Hierarchical Bayesian Classifier Combination (HBCC) is for discrete classifiers and assumes that the individual classifiers are conditionally independent given the true class label. The comparison of HBCC with majority voting on six benchmark classification data sets shows that it generally outperforms majority voting in the classification accuracy.

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Notes

  1. 1.

    The Data Generation Model (DGM) is the model that produced the observed data.

  2. 2.

    For a given value of \(t^{*}\) and \(x^{*}\) the distribution \(p(t^{*}|x^{*},h)\) depends only on h and remains constant for all values of \(\mathcal {D}\). It means that the random variable \(t^{*}|x^{*}\) is conditionally independent of \(\mathcal {D}\) given h which yields \( \mathcal {P}(t^{*}|x^{*},h)= \mathcal {P}(t^{*}|x^{*},h,\mathcal {D})\) .

  3. 3.

    Wilcoxon signed rank test with continuity-correction; the alternative hypothesis was: the mean accuracy of HBCC is equal to the mean accuracy of majority voting.

  4. 4.

    Look at the difference between bold values in Table 4 and HBCC or majority voting results in Table 3.

  5. 5.

    The computational complexity of majority voting is O(JK), where J and K are respectively the number of the true class labels and the number of individual classifiers used in the combination.

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Authors and Affiliations

  1. Audio & Speech Processing Lab, Computer Engineering Department, Iran University of Science and Technology, Tehran, Iran

    Mohammad Ghasemi Hamed & Ahmad Akbari

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  1. Mohammad Ghasemi Hamed
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  2. Ahmad Akbari
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Correspondence to Mohammad Ghasemi Hamed or Ahmad Akbari .

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  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

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Ghasemi Hamed, M., Akbari, A. (2018). Hierarchical Bayesian Classifier Combination. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-96136-1_10

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