A Swarm Intelligence Approach in Undersampling Majority Class

Alhakbani, Haya Abdullah and al-Rifaie, Mohammad Majid. 2016. 'A Swarm Intelligence Approach in Undersampling Majority Class'. In: 10th International Conference, ANTS 2016, Brussels, Belgium, September 7-9, 2016, Proceedings. Brussels, Belgium. [Conference or Workshop Item]

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Abstract or Description

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.

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Conference or Workshop Item (Paper)

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28 August 2016Published

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Brussels, Belgium

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Date Deposited:

06 Sep 2016 09:17

Last Modified:

29 Apr 2020 16:20



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