Goldsmiths - University of London

Exploring Feature-Level Duplications on Imbalanced Data Using Stochastic Diffusion Search

Alhakbani, Haya Abdullah and al-Rifaie, Mohammad Majid. 2017. 'Exploring Feature-Level Duplications on Imbalanced Data Using Stochastic Diffusion Search'. In: EUMAS. Valencia, Spain. [Conference or Workshop Item]

2016_EUMAS_Feature-Level Duplications.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (426kB) | Preview

Abstract or Description

One of the computer algorithms inspired by swarm intelligence is stochastic diffusion search (SDS). SDS uses some of the processes and techniques found in swarm to solve search and optimisation problems. In this paper, a hybrid approach is proposed to deal with real-world imbalanced data. The proposed model involves oversampling the minority class, undersampling the majority class as well as optimising the parameters of the classifier, Support Vector Machine (SVM). The proposed model uses Synthetic Minority Over-sampling Technique (SMOTE) to perform the oversampling and the agents of a swarm intelligence technique, SDS, to perform an `informed' undersampling on the majority classes. In addition to comparing the agents-led undersampling with random undersampling, the results are contrasted against other best known techniques on nine real-world datasets. Moreover, the behaviour of SDS agents in this context is also analysed.

Item Type: Conference or Workshop Item (Paper)

Identification Number (DOI):


Departments, Centres and Research Units:



23 June 2017Published Online

Event Location:

Valencia, Spain

Item ID:


Date Deposited:

26 Sep 2017 11:34

Last Modified:

07 Nov 2017 10:54

URI: http://research.gold.ac.uk/id/eprint/21039

View statistics for this item...

Edit Record Edit Record (login required)