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...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
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Taylor & Francis Group
2018-04-01
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Series: | Journal of Information and Telecommunication |
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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. |
first_indexed | 2024-12-19T23:24:38Z |
format | Article |
id | doaj.art-66e40881229e44929982c75b4c4b2e3d |
institution | Directory Open Access Journal |
issn | 2475-1839 2475-1847 |
language | English |
last_indexed | 2024-12-19T23:24:38Z |
publishDate | 2018-04-01 |
publisher | Taylor & Francis Group |
record_format | Article |
series | Journal of Information and Telecommunication |
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 |
work_keys_str_mv | AT joannajedrzejowicz imbalanceddataclassificationusingmapreduceandrelief AT robertkostrzewski imbalanceddataclassificationusingmapreduceandrelief AT jakubneumann imbalanceddataclassificationusingmapreduceandrelief AT magdalenazakrzewska imbalanceddataclassificationusingmapreduceandrelief |