Improving outliers detection in data streams using LiCS and voting

Detecting outliers in real-time is increasingly important for many real-world applications such as detecting abnormal heart activity, intrusions to systems, spams or abnormal credit card transactions. However, detecting outliers in data streams rises many challenges such as high-dimensionality, dyna...

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Autores principales:	Fatima-Zahra Benjelloun, Ahmed Oussous, Amine Bennani, Samir Belfkih, Ayoub Ait Lahcen
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Data streams Outlier detection High-dimensional data Big data mining Intrusion detection Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/27690865aa2042bcb85cf54db30f0f6b
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spelling	oai:doaj.org-article:27690865aa2042bcb85cf54db30f0f6b2021-11-22T04:19:40ZImproving outliers detection in data streams using LiCS and voting1319-157810.1016/j.jksuci.2019.08.003https://doaj.org/article/27690865aa2042bcb85cf54db30f0f6b2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1319157819301454https://doaj.org/toc/1319-1578Detecting outliers in real-time is increasingly important for many real-world applications such as detecting abnormal heart activity, intrusions to systems, spams or abnormal credit card transactions. However, detecting outliers in data streams rises many challenges such as high-dimensionality, dynamic data distribution and unpredictable relationships. Our simulations demonstrate that some advanced solutions still show drawbacks. In this paper, first, we improve the capacity to detect outliers of both micro-clusters based algorithms (MCOD) and distance-based algorithms (Abstract-C and Exact-Storm) known for their performance. This is by adding a layer called LiCS that classifies online the K-nearest-neighbors (Knn) of each node based on their evolutionary status. This layer aggregates the results and uses a count threshold to better classify nodes. Experiments on SpamBase datasets confirmed that our technique enhances the accuracy and the precision of such algorithm and helps to reduce the unclassified nodes.Second, we propose a hybrid solution based on iterative majority voting and our LiCS. Experiments on real data proves that it outperforms discussed algorithms in terms of accuracy, precision and sensitivity in detecting outliers. It also minimizes the issue of unclassified instances and consolidate the different outputs of algorithms.Fatima-Zahra BenjellounAhmed OussousAmine BennaniSamir BelfkihAyoub Ait LahcenElsevierarticleData streamsOutlier detectionHigh-dimensional dataBig data miningIntrusion detectionElectronic computers. Computer scienceQA75.5-76.95ENJournal of King Saud University: Computer and Information Sciences, Vol 33, Iss 10, Pp 1177-1185 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Data streams Outlier detection High-dimensional data Big data mining Intrusion detection Electronic computers. Computer science QA75.5-76.95
spellingShingle	Data streams Outlier detection High-dimensional data Big data mining Intrusion detection Electronic computers. Computer science QA75.5-76.95 Fatima-Zahra Benjelloun Ahmed Oussous Amine Bennani Samir Belfkih Ayoub Ait Lahcen Improving outliers detection in data streams using LiCS and voting
description	Detecting outliers in real-time is increasingly important for many real-world applications such as detecting abnormal heart activity, intrusions to systems, spams or abnormal credit card transactions. However, detecting outliers in data streams rises many challenges such as high-dimensionality, dynamic data distribution and unpredictable relationships. Our simulations demonstrate that some advanced solutions still show drawbacks. In this paper, first, we improve the capacity to detect outliers of both micro-clusters based algorithms (MCOD) and distance-based algorithms (Abstract-C and Exact-Storm) known for their performance. This is by adding a layer called LiCS that classifies online the K-nearest-neighbors (Knn) of each node based on their evolutionary status. This layer aggregates the results and uses a count threshold to better classify nodes. Experiments on SpamBase datasets confirmed that our technique enhances the accuracy and the precision of such algorithm and helps to reduce the unclassified nodes.Second, we propose a hybrid solution based on iterative majority voting and our LiCS. Experiments on real data proves that it outperforms discussed algorithms in terms of accuracy, precision and sensitivity in detecting outliers. It also minimizes the issue of unclassified instances and consolidate the different outputs of algorithms.
format	article
author	Fatima-Zahra Benjelloun Ahmed Oussous Amine Bennani Samir Belfkih Ayoub Ait Lahcen
author_facet	Fatima-Zahra Benjelloun Ahmed Oussous Amine Bennani Samir Belfkih Ayoub Ait Lahcen
author_sort	Fatima-Zahra Benjelloun
title	Improving outliers detection in data streams using LiCS and voting
title_short	Improving outliers detection in data streams using LiCS and voting
title_full	Improving outliers detection in data streams using LiCS and voting
title_fullStr	Improving outliers detection in data streams using LiCS and voting
title_full_unstemmed	Improving outliers detection in data streams using LiCS and voting
title_sort	improving outliers detection in data streams using lics and voting
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/27690865aa2042bcb85cf54db30f0f6b
work_keys_str_mv	AT fatimazahrabenjelloun improvingoutliersdetectionindatastreamsusinglicsandvoting AT ahmedoussous improvingoutliersdetectionindatastreamsusinglicsandvoting AT aminebennani improvingoutliersdetectionindatastreamsusinglicsandvoting AT samirbelfkih improvingoutliersdetectionindatastreamsusinglicsandvoting AT ayoubaitlahcen improvingoutliersdetectionindatastreamsusinglicsandvoting
_version_	1718418212681416704

Improving outliers detection in data streams using LiCS and voting

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