Imbalanced data classification using MapReduce and relief

Classification of imbalanced data has been reported to require modification of standard classification algorithms and lately has attracted a lot of attention due to practical applications in industry, banking and finance. The aim of the paper is to examine algorithms known from literature when two m...

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Main Authors: Joanna Jedrzejowicz, Robert Kostrzewski, Jakub Neumann, Magdalena Zakrzewska
Format: Article
Language:English
Published: Taylor & Francis Group 2018-04-01
Series:Journal of Information and Telecommunication
Subjects:
Online Access:http://dx.doi.org/10.1080/24751839.2018.1440454
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author Joanna Jedrzejowicz
Robert Kostrzewski
Jakub Neumann
Magdalena Zakrzewska
author_facet Joanna Jedrzejowicz
Robert Kostrzewski
Jakub Neumann
Magdalena Zakrzewska
author_sort Joanna Jedrzejowicz
collection DOAJ
description Classification of imbalanced data has been reported to require modification of standard classification algorithms and lately has attracted a lot of attention due to practical applications in industry, banking and finance. The aim of the paper is to examine algorithms known from literature when two modifications are introduced: MapReduce to parallelize computations and Relief to select most valuable attributes. Both modifications are needed in Big Data area. Also two new algorithms are considered.
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spelling doaj.art-66e40881229e44929982c75b4c4b2e3d2022-12-21T20:01:53ZengTaylor & Francis GroupJournal of Information and Telecommunication2475-18392475-18472018-04-012221723010.1080/24751839.2018.14404541440454Imbalanced data classification using MapReduce and reliefJoanna Jedrzejowicz0Robert Kostrzewski1Jakub Neumann2Magdalena ZakrzewskaUniversity of GdanskUniversity of GdanskUniversity of GdanskClassification of imbalanced data has been reported to require modification of standard classification algorithms and lately has attracted a lot of attention due to practical applications in industry, banking and finance. The aim of the paper is to examine algorithms known from literature when two modifications are introduced: MapReduce to parallelize computations and Relief to select most valuable attributes. Both modifications are needed in Big Data area. Also two new algorithms are considered.http://dx.doi.org/10.1080/24751839.2018.1440454Imbalanced dataclassificationparallelizationfeature selection
spellingShingle Joanna Jedrzejowicz
Robert Kostrzewski
Jakub Neumann
Magdalena Zakrzewska
Imbalanced data classification using MapReduce and relief
Journal of Information and Telecommunication
Imbalanced data
classification
parallelization
feature selection
title Imbalanced data classification using MapReduce and relief
title_full Imbalanced data classification using MapReduce and relief
title_fullStr Imbalanced data classification using MapReduce and relief
title_full_unstemmed Imbalanced data classification using MapReduce and relief
title_short Imbalanced data classification using MapReduce and relief
title_sort imbalanced data classification using mapreduce and relief
topic Imbalanced data
classification
parallelization
feature selection
url http://dx.doi.org/10.1080/24751839.2018.1440454
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AT robertkostrzewski imbalanceddataclassificationusingmapreduceandrelief
AT jakubneumann imbalanceddataclassificationusingmapreduceandrelief
AT magdalenazakrzewska imbalanceddataclassificationusingmapreduceandrelief