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This article is part of the supplement: Eighth International Conference on Bioinformatics (InCoB2009): Computational Biology

Open Access Open Badges Proceedings

A particle swarm based hybrid system for imbalanced medical data sampling

Pengyi Yang12*, Liang Xu3, Bing B Zhou1*, Zili Zhang34 and Albert Y Zomaya15

Author Affiliations

1 School of Information Technologies (J12), The University of Sydney, NSW 2006, Australia

2 National ICT Australia, Australian Technology Park, Eveleigh, NSW 2015, Australia

3 Faculty of Computer and Information Science, Southwest University, CQ 400715, PR China

4 School of Information Technology, Deakin University, Geelong, VIC 3217, Australia

5 Sydney Bioinformatics Center and the Center for Mathematical Biology, The University of Sydney, NSW 2006, Australia

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BMC Genomics 2009, 10(Suppl 3):S34  doi:10.1186/1471-2164-10-S3-S34

Published: 3 December 2009



Medical and biological data are commonly with small sample size, missing values, and most importantly, imbalanced class distribution. In this study we propose a particle swarm based hybrid system for remedying the class imbalance problem in medical and biological data mining. This hybrid system combines the particle swarm optimization (PSO) algorithm with multiple classifiers and evaluation metrics for evaluation fusion. Samples from the majority class are ranked using multiple objectives according to their merit in compensating the class imbalance, and then combined with the minority class to form a balanced dataset.


One important finding of this study is that different classifiers and metrics often provide different evaluation results. Nevertheless, the proposed hybrid system demonstrates consistent improvements over several alternative methods with three different metrics. The sampling results also demonstrate good generalization on different types of classification algorithms, indicating the advantage of information fusion applied in the hybrid system.


The experimental results demonstrate that unlike many currently available methods which often perform unevenly with different datasets the proposed hybrid system has a better generalization property which alleviates the method-data dependency problem. From the biological perspective, the system provides indication for further investigation of the highly ranked samples, which may result in the discovery of new conditions or disease subtypes.