Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data

Applying machine learning algorithms for assessing the transmission quality in optical networks is associated with substantial challenges. Datasets that could provide training instances tend to be small and heavily imbalanced. This requires applying imbalanced compensation techniques when using bina...

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Autores principales:	Paweł Cichosz, Stanisław Kozdrowski, Sławomir Sujecki
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning optical networks imbalanced data one-class classification Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/7ed8b64142234126b3d69029095a2866
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spelling	oai:doaj.org-article:7ed8b64142234126b3d69029095a28662021-11-25T17:30:19ZLearning to Classify DWDM Optical Channels from Tiny and Imbalanced Data10.3390/e231115041099-4300https://doaj.org/article/7ed8b64142234126b3d69029095a28662021-11-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1504https://doaj.org/toc/1099-4300Applying machine learning algorithms for assessing the transmission quality in optical networks is associated with substantial challenges. Datasets that could provide training instances tend to be small and heavily imbalanced. This requires applying imbalanced compensation techniques when using binary classification algorithms, but it also makes one-class classification, learning only from instances of the majority class, a noteworthy alternative. This work examines the utility of both these approaches using a real dataset from a Dense Wavelength Division Multiplexing network operator, gathered through the network control plane. The dataset is indeed of a very small size and contains very few examples of “bad” paths that do not deliver the required level of transmission quality. Two binary classification algorithms, random forest and extreme gradient boosting, are used in combination with two imbalance handling methods, instance weighting and synthetic minority class instance generation. Their predictive performance is compared with that of four one-class classification algorithms: One-class SVM, one-class naive Bayes classifier, isolation forest, and maximum entropy modeling. The one-class approach turns out to be clearly superior, particularly with respect to the level of classification precision, making it possible to obtain more practically useful models.Paweł CichoszStanisław KozdrowskiSławomir SujeckiMDPI AGarticlemachine learningoptical networksimbalanced dataone-class classificationScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1504, p 1504 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning optical networks imbalanced data one-class classification Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	machine learning optical networks imbalanced data one-class classification Science Q Astrophysics QB460-466 Physics QC1-999 Paweł Cichosz Stanisław Kozdrowski Sławomir Sujecki Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data
description	Applying machine learning algorithms for assessing the transmission quality in optical networks is associated with substantial challenges. Datasets that could provide training instances tend to be small and heavily imbalanced. This requires applying imbalanced compensation techniques when using binary classification algorithms, but it also makes one-class classification, learning only from instances of the majority class, a noteworthy alternative. This work examines the utility of both these approaches using a real dataset from a Dense Wavelength Division Multiplexing network operator, gathered through the network control plane. The dataset is indeed of a very small size and contains very few examples of “bad” paths that do not deliver the required level of transmission quality. Two binary classification algorithms, random forest and extreme gradient boosting, are used in combination with two imbalance handling methods, instance weighting and synthetic minority class instance generation. Their predictive performance is compared with that of four one-class classification algorithms: One-class SVM, one-class naive Bayes classifier, isolation forest, and maximum entropy modeling. The one-class approach turns out to be clearly superior, particularly with respect to the level of classification precision, making it possible to obtain more practically useful models.
format	article
author	Paweł Cichosz Stanisław Kozdrowski Sławomir Sujecki
author_facet	Paweł Cichosz Stanisław Kozdrowski Sławomir Sujecki
author_sort	Paweł Cichosz
title	Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data
title_short	Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data
title_full	Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data
title_fullStr	Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data
title_full_unstemmed	Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data
title_sort	learning to classify dwdm optical channels from tiny and imbalanced data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7ed8b64142234126b3d69029095a2866
work_keys_str_mv	AT pawełcichosz learningtoclassifydwdmopticalchannelsfromtinyandimbalanceddata AT stanisławkozdrowski learningtoclassifydwdmopticalchannelsfromtinyandimbalanceddata AT sławomirsujecki learningtoclassifydwdmopticalchannelsfromtinyandimbalanceddata
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Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data

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