title: Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.
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 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.
authors.forename: Mohsen
authors.initials: M
authors.lastname: Hajiloo
authors.forename: Hamid R
authors.initials: HR
authors.lastname: Rabiee
authors.forename: Mahdi
authors.initials: M
authors.lastname: Anooshahpour
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003110:Colonic Neoplasms / Q000145:classification / Q000235:genetics; D019295:Computational Biology; D003663:Decision Trees; D017143:Fuzzy Logic; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D007938:Leukemia / Q000145:classification / Q000235:genetics; D008297:Male; D011471:Prostatic Neoplasms / Q000145:classification / Q000235:genetics; D040081:Protein Array Analysis / Q000379:methods*; D012984:Software; D060388:Support Vector Machine