BACKGROUND: 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.
RESULTS: 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.
CONCLUSIONS: 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.