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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Classification of Unbalanced Datasets and Detection of Rare Events in Industry: Issues and Solutions

verfasst von : Marco Vannucci, Valentina Colla

Erschienen in: Engineering Applications of Neural Networks

Verlag: Springer International Publishing

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Abstract

Classification of unbalanced datasets is a critical task that is getting interest due to its relevance in many contexts and especially in the industrial one where machine faults, quality deviations belong to the class of rare events whose identification is fundamental. This work introduces and outlines the main themes related to this problem including an analysis of the factors that make the detection of unfrequent events complicated, a list of the metrics used for classifiers assessment and a review of most popular and emerging approaches used for facing class unbalance with a special focus on the detection of rare events.

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Vorheriges Kapitel Bio-inspired Audio-Visual Speech Recognition Towards the Zero Instruction Set Computing
Nächstes Kapitel Variable Selection for Efficient Design of Machine Learning-Based Models: Efficient Approaches for Industrial Applications
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Metadaten
Titel
Classification of Unbalanced Datasets and Detection of Rare Events in Industry: Issues and Solutions
verfasst von
Marco Vannucci
Valentina Colla
Copyright-Jahr
2016
Buch
Engineering Applications of Neural Networks

Print ISBN: 978-3-319-44187-0

Electronic ISBN: 978-3-319-44188-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-44188-7_26

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