| Abstract | 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 interpret ability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for micro-array classification.
Results:
Experimental results on public leukemia, prostate, and colon cancer data-sets 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 micro-array 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
micro array data.
Conclusions:
Fuzzy support vector machine as a new classification model with high generalization power,
robustness, and good interpret ability seems to be a promising tool for gene expression micro array classification. |
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