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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 1/2019

06.07.2018 | Regular Paper

Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise

verfasst von: Ronaldo C. Prati, Julián Luengo, Francisco Herrera

Erschienen in: Knowledge and Information Systems | Ausgabe 1/2019

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Abstract

The problem of class noisy instances is omnipresent in different classification problems. However, most of research focuses on noise handling in binary classification problems and adaptations to multiclass learning. This paper aims to contextualize noise labels in the context of non-binary classification problems, including multiclass, multilabel, multitask, multi-instance ordinal and data stream classification. Practical considerations for analyzing noise under these classification problems, as well as trends, open-ended problems and future research directions are analyzed. We believe this paper could help expand research on class noise handling and help practitioners to better identify the particular aspects of noise in challenging classification scenarios.
Vorheriger Artikel Spatiotemporal traffic network analysis: technology and applications
Nächster Artikel Modeling and implementing distributed data mining strategies in JaCa-DDM

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Fußnoten
1
We opt to use artificially generated data to have control about data characteristics.
 
2
In this particular simple problem, some algorithms are more resilient to class noise for low noise rates, although we can observe this trend for higher noise rates.
 
3
A possible explanation for this behavior of J48 is related to the uncertainty in leaf boundaries in the tree
 
4
Multilabel classification should not be confused with multiclass classification (Sect. 3.1), which is the problem of categorizing instances into one of more than two classes.
 
7
This corresponds to noise at random. For the noise completely at random case, we have \(p_1 = p_2 = \ldots = p_n = p_e\).
 
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Metadaten
Titel
Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise
verfasst von
Ronaldo C. Prati
Julián Luengo
Francisco Herrera
Publikationsdatum
06.07.2018
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 1/2019
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-018-1244-4

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