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A swarm intelligence approach in undersampling majority class

A swarm intelligence approach in undersampling majority class

Alhakbani, Haya Abdullah and Al-Rifaie, Mohammad Majid ORCID: 0000-0002-1798-9615 (2016) A swarm intelligence approach in undersampling majority class. In: 10th International Conference, ANTS 2016, Brussels, Belgium, September 7-9, 2016, Proceedings. Lecture Notes in Computer Science, 9882 . Springer, Cham, Switzerland, pp. 225-232. ISBN 978-3319444277 ISSN 0302-9743 (Print), 1611-3349 (Online) (doi:https://doi.org/10.1007/978-3-319-44427-7_19)

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Abstract

Over the years, machine learning has been facing the issue of imbalance dataset. It occurs when the number of instances in one class significantly outnumbers the instances in the other class. This study investigates a new approach for balancing the dataset using a swarm intelligence technique, Stochastic Diffusion Search (SDS), to undersample the majority class on a direct marketing dataset. The outcome of the novel application of this swarm intelligence algorithm demonstrates promising results which encourage the possibility of undersampling a majority class by removing redundant data whist protecting the useful data in the dataset. This paper details the behaviour of the proposed algorithm in dealing with this problem and investigates the results which are contrasted against other techniques.

Item Type: Conference Proceedings
Title of Proceedings: 10th International Conference, ANTS 2016, Brussels, Belgium, September 7-9, 2016, Proceedings
Uncontrolled Keywords: swarm intelligence, class imbalance, stochastic diffusion search, SVM
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Centre for Computer & Computational Science
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 01 Jul 2021 16:11
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/20995

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