  The Fuzzy Gene Filter (FGF) is an optimised Fuzzy Inference System designed
to rank genes in order of differential expression, based on expression data
generated in a microarray experiment. This paper examines the effectiveness of
the FGF for feature selection using various classification architectures. The
FGF is compared to three of the most common gene ranking algorithms: t-test,
Wilcoxon test and ROC curve analysis. Four classification schemes are used to
compare the performance of the FGF vis-a-vis the standard approaches: K Nearest
Neighbour (KNN), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC)
and Artificial Neural Network (ANN). A nested stratified Leave-One-Out Cross
Validation scheme is used to identify the optimal number top ranking genes, as
well as the optimal classifier parameters. Two microarray data sets are used
for the comparison: a prostate cancer data set and a lymphoma data set.
