Optimization of data resampling through GA for the classification of imbalanced datasets

International Journal of Advances in Intelligent Informatics

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Title Optimization of data resampling through GA for the classification of imbalanced datasets
Creator Galli, Filippo
Vannucci, Marco
Colla, Valentina
Subject Imbalanced datasets; Classification; Data resampling; Genetic algorithm
Description Classification of imbalanced datasets is a critical problem in numerous contexts. In these applications, standard methods are not able to satisfactorily detect rare patterns due to multiple factors that bias the classifiers toward the frequent class. This paper overview a novel family of methods for the resampling of an imbalanced dataset in order to maximize the performance of arbitrary data-driven classifiers. The presented approaches exploit genetic algorithms (GA) for the optimization of the data selection process according to a set of criteria that assess each candidate sample suitability. A comparison among the presented techniques on a set of industrial and literature datasets put into evidence the validity of this family of approaches, which is able not only to improve the performance of a standard classifier but also to determine the optimal resampling rate automatically. Future activities for the improvement of the proposed approach will include the development of new criteria for the assessment of sample suitability.
Publisher Universitas Ahmad Dahlan
Date 2019-10-29
Type info:eu-repo/semantics/article

Format application/pdf
Identifier http://ijain.org/index.php/IJAIN/article/view/409
Source International Journal of Advances in Intelligent Informatics; Vol 5, No 3 (2019): November 2019; 297-307
Language eng
Relation http://ijain.org/index.php/IJAIN/article/view/409/ijain_v4i3_p297-307
Rights https://creativecommons.org/licenses/by-sa/4.0

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