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

BMC Bioinformatics

Research

Hamid R Rabiee¹  Mahdi Anooshahpour¹  Mohsen Hajiloo² 

[1] Department of Computer Engineering, Sharif University of Technology, Azadi Ave., Tehran, Iran;Department of Computer Engineering, Sharif University of Technology, Azadi Ave., Tehran, Iran;Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada;

关键词: Support Vector Machine;    Feature Selection Method;    Fuzzy Support Vector Machine;    Mercer Kernel;    Diagonal Linear Discriminant Analysis;   

DOI  :  10.1186/1471-2105-14-S13-S4

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsExperimental 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.ConclusionsFuzzy 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.

【 授权许可】

CC BY   
© Hajiloo et al; licensee BioMed Central Ltd. 2013