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This article is part of the supplement: Selected articles from the 9th Annual Biotechnology and Bioinformatics Symposium (BIOT 2012)

Open Access Open Badges Research

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays

Mohsen Hajiloo12*, Hamid R Rabiee1 and Mahdi Anooshahpour1

Author Affiliations

1 Department of Computer Engineering, Sharif University of Technology, Azadi Ave., Tehran, Iran

2 Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada

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BMC Bioinformatics 2013, 14(Suppl 13):S4  doi:10.1186/1471-2105-14-S13-S4

Published: 1 October 2013



The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification.


Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data.


Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.