Figure 3.

Gene lists are input to classifiers: training and test cross validation. Each feature selection method was applied to training datasets that contained i) 50% of samples, ii) 20 samples (10 from each class) or iii) 10 samples (5 from each class), and the most highly ranked genes were selected to generate gene lists of length between 2 and 100 genes. The ability of these gene lists to form successful classifiers was evaluated. The graphs (A) show the prediction success (cumulative RCI values) of these when applied to all 9 datasets and evaluated using four classification tools. Note that the scale of Y-axis (cumulative RCI value) is different between plots. The bar plots (B) show average RCI values showing the success of the top 40 genes, selected by 10 feature selection methods, to form classifiers which can predict the class of blind test data for each of the 9 datasets.

Jeffery et al. BMC Bioinformatics 2006 7:359   doi:10.1186/1471-2105-7-359
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