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

摘要

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.