Table 1.
Brief description of the datasets along with the corresponding number of features, observations, class-wise distributions and sources.
Fig 1.
Flowchart of the proposed method.
Table 2.
Classification error rates produced by different methods on various subsets of genes.
Table 3.
Win-loss table of the methods used.
Total number of wins of the methods on the data sets is given in the last row of the table.
Fig 2.
Bar-plots of error rates of the proposed and the other classical methods on various subsets for Leukemia dataset.
Fig 3.
Bar-plots of error rates of the proposed and the other classical methods on various subsets of genes for Colon dataset.
Fig 4.
Bar-plots of error rates of the proposed and the other classical methods on various subsets of genes for Lungcancer dataset.
Fig 5.
Bar-plots of error rates of the proposed and the other classical methods on various subsets of genes for Srbct dataset.
Fig 6.
Bar-plots of error rates of the proposed and the other classical methods on various subsets of genes for DLBCL dataset.
Fig 7.
Bar-plots of error rates of the proposed and the other classical methods on various subsets of genes for Breast dataset.
Fig 8.
Bar-plots of error rates of the proposed and the other classical methods on various subsets of genes for TumorC dataset.
Fig 9.
Bar-plots of error rates of the proposed and the other classical methods on various subsets of genes for Prostate dataset.
Fig 10.
Box-plots of the error rates produced by random forest, using top 10 features selected by different feature selection methods for Leukemia dataset.
Fig 11.
Box-plots of the error rates produced by random forest, using top 10 features selected by different feature selection methods for Colon dataset.
Fig 12.
Box-plots of the error rates produced by random forest, using top 10 features selected by different feature selection methods for Lungcancer dataset.
Fig 13.
Box-plots of the error rates produced by random forest, using top 10 features selected by different feature selection methods for Srbct dataset.
Fig 14.
Box-plots of the error rates produced by random forest, using top 10 features selected by different feature selection methods for DLBCL dataset.
Fig 15.
Box-plots of the error rates produced by random forest, using top 10 features selected by different feature selection methods for Breastcancer dataset.
Fig 16.
Box-plots of the error rates produced by random forest, using top 10 features selected by different feature selection methods for TumorC dataset.
Fig 17.
Box-plots of the error rates produced by random forest, using top 10 features selected by different feature selection methods for Prostate dataset.
Table 4.
Classification error rates produced by different methods on simulated data.
Fig 18.
Bar-plots of errors produced by different feature selection methods on simulated data having outliers, for various subsets of genes.
Fig 19.
Bar-plots of errors produced by different feature selection methods on simulated data, having no outliers, for various subsets of genes.