Fuzzy Support Vector Machine: an Efficient Rule-Based Classification Technique for Microarrays

TitleFuzzy Support Vector Machine: an Efficient Rule-Based Classification Technique for Microarrays
Publication TypeJournal Article
Year of Publication2012
AuthorsHajiloo, M., H. R. Rabiee, and M. Anooshahpour
JournalBMC Bioinformatics
IssueSupplement 13
Date Published10/2012
AbstractBackground: 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.
DOI<a href="http://dx.doi.org/10.1186/1471-2105-14-S13-S4&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;