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Classification Imbalanced Data Sets: A Survey

by Shrouk El-Amir, Heba El-Fiqi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 23
Year of Publication: 2019
Authors: Shrouk El-Amir, Heba El-Fiqi
10.5120/ijca2019919682

Shrouk El-Amir, Heba El-Fiqi . Classification Imbalanced Data Sets: A Survey. International Journal of Computer Applications. 177, 23 ( Dec 2019), 20-23. DOI=10.5120/ijca2019919682

@article{ 10.5120/ijca2019919682,
author = { Shrouk El-Amir, Heba El-Fiqi },
title = { Classification Imbalanced Data Sets: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2019 },
volume = { 177 },
number = { 23 },
month = { Dec },
year = { 2019 },
issn = { 0975-8887 },
pages = { 20-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number23/31037-2019919682/ },
doi = { 10.5120/ijca2019919682 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:46:42.252966+05:30
%A Shrouk El-Amir
%A Heba El-Fiqi
%T Classification Imbalanced Data Sets: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 23
%P 20-23
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Unbalanced data, a snag often found in real-world applications, can seriously adversely affect machine learning algorithms ' classification efficiency. Various tries are made to classify unbalanced data sets. In order to face the imbalanced data sets snag, we should rebalance them artificially through machine learning classifiers by oversampling and/or under-sampling.

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Index Terms

Computer Science
Information Sciences

Keywords

Imbalance dataset sampling cost-sensitive learning imbalance ratio

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