Classification Imbalanced Data Sets: A Survey
Author(s) -
Shrouk El-Amir,
Heba El-Fiqi
Publication year - 2019
Publication title -
international journal of computer applications
Language(s) - English
Resource type - Journals
ISSN - 0975-8887
DOI - 10.5120/ijca2019919682
Subject(s) - computer science , data science , information retrieval , artificial intelligence , data mining
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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