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A Weight-Selection Strategy on Training Deep Neural Networks for Imbalanced Classification Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Classification Using Mixture of Discriminative Learners: The Case of Compositional Data

Authors : Elvis Togban, Djemel Ziou

Published in: Image Analysis and Recognition

Publisher: Springer International Publishing

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Abstract

Compositional data arise in many fields and their analysis has to be done with care since these data are bounded and summing up to a constant. In this paper, we propose a mixture model which combines several discriminative models through a set of Dirichlet-based weights. It is worth noticing that the Dirichlet distribution is not used here as a prior to the mixing coefficients but instead to model the repartition of the tasks among the classifiers. By doing so, we do not need to transform the data while keeping interpretable results. Experiments on synthetic and real-world data sets show the efficiency of our model.

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previous chapter Classification Boosting by Data Decomposition Using Consensus-Based Combination of Classifiers
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Metadata
Title
Classification Using Mixture of Discriminative Learners: The Case of Compositional Data
Authors
Elvis Togban
Djemel Ziou
Copyright Year
2017
Book
Image Analysis and Recognition

Print ISBN: 978-3-319-59875-8

Electronic ISBN: 978-3-319-59876-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59876-5_46

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