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Erschienen in:
Learning Sequential Data with the Help of Linear Systems Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Co-training with Credal Models

verfasst von : Yann Soullard, Sébastien Destercke, Indira Thouvenin

Erschienen in: Artificial Neural Networks in Pattern Recognition

Verlag: Springer International Publishing

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Abstract

So-called credal classifiers offer an interesting approach when the reliability or robustness of predictions have to be guaranteed. Through the use of convex probability sets, they can select multiple classes as prediction when information is insufficient and predict a unique class only when the available information is rich enough. The goal of this paper is to explore whether this particular feature can be used advantageously in the setting of co-training, in which a classifier strengthen another one by feeding it with new labeled data. We propose several co-training strategies to exploit the potential indeterminacy of credal classifiers and test them on several UCI datasets. We then compare the best strategy to the standard co-training process to check its efficiency.

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Vorheriges Kapitel Density Based Clustering via Dominant Sets
Nächstes Kapitel Interpretable Classifiers in Precision Medicine: Feature Selection and Multi-class Categorization
Fußnoten
1
We use capital letter to denote the fact that the returned prediction may be a set of classes.
 
2
We use \(40\,\%\) of them as labeled instances to get a compromise between having a large part of data as unlabeled and having a sufficiently large labeled dataset to reduce the sampling bias.
 
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Metadaten
Titel
Co-training with Credal Models
verfasst von
Yann Soullard
Sébastien Destercke
Indira Thouvenin
Copyright-Jahr
2016
Buch
Artificial Neural Networks in Pattern Recognition

Print ISBN: 978-3-319-46181-6

Electronic ISBN: 978-3-319-46182-3

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46182-3_8