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Pattern Analysis and Applications

30.08.2018 | Theoretical Advances

Towards instance-dependent label noise-tolerant classification: a probabilistic approach

verfasst von: Jakramate Bootkrajang, Jeerayut Chaijaruwanich

Erschienen in: Pattern Analysis and Applications

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Abstract

Learning from labelled data is becoming more and more challenging due to inherent imperfection of training labels. Existing label noise-tolerant learning machines were primarily designed to tackle class-conditional noise which occurs at random, independently from input instances. However, relatively less attention was given to a more general type of label noise which is influenced by input features. In this paper, we try to address the problem of learning a classifier in the presence of instance-dependent label noise by developing a novel label noise model which is expected to capture the variation of label noise rate within a class. This is accomplished by adopting a probability density function of a mixture of Gaussians to approximate the label flipping probabilities. Experimental results demonstrate the effectiveness of the proposed method over existing approaches.

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Fußnoten
1
We used LIBLINEAR [31] in this study.
 
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Metadaten
Titel
Towards instance-dependent label noise-tolerant classification: a probabilistic approach
verfasst von
Jakramate Bootkrajang
Jeerayut Chaijaruwanich
Publikationsdatum
30.08.2018
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-018-0750-z

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