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Table 1 The best performing gene selection procedure with the .632+ bootstrap error identified by FiGS for each of the six microarray datasets.

From: FiGS: a filter-based gene selection workbench for microarray data

Dataset

Feature selection method

k

Gene expression pattern

Feature discretization

Feature vector addition

Classifier

Error

Leukemia

Wilcoxon rank sum test

10

Down-regulated

Not apply

Not apply

SVM

0.02

Leukemia

Wilcoxon rank sum test

10

Down-regulated

Not apply

Not apply

RF

0.02

Leukemia

Wilcoxon rank sum test

10

Down-regulated

Not apply

Apply

SVM

0.02

Leukemia

Wilcoxon rank sum test

10

Down-regulated

Not apply

Apply

RF

0.02

Leukemia

Wilcoxon rank sum test

10

Down-regulated

Apply

Not apply

SVM

0.02

Leukemia

Information gain method

10

Down-regulated

Not apply

Not apply

SVM

0.02

Leukemia

Information gain method

10

Down-regulated

Not apply

Not apply

RF

0.02

Leukemia

Information gain method

10

Down-regulated

Not apply

Apply

SVM

0.02

Leukemia

Information gain method

10

Down-regulated

Not apply

Apply

RF

0.02

Leukemia

Information gain method

10

Down-regulated

Apply

Not apply

SVM

0.02

Leukemia

Information gain method

10

Down-regulated

Apply

Not apply

RF

0.02

Colon

Information gain method

30

Up-regulated

Not apply

Not apply

RF

0.11

Prostate

Information gain method

25

Total

Not apply

Not apply

RF

0.05

Adenocarcinoma

Wilcoxon rank sum test

10

Up-regulated

Not apply

Not apply

RF

0.10

Breast

Wilcoxon rank sum test

15

Down-regulated

Not apply

Apply

SVM

0.31

Breast

Information gain method

15

Down-regulated

Not apply

Apply

SVM

0.31

DLBCL

Wilcoxon rank sum test

20

Total

Not apply

Not apply

RF

0.08

  1. k is the number of selected genes; and error is the .632+ bootstrap error achieved by the best performing gene selection procedure tested on 100 bootstrap samples. In the case of the leukemia and breast datasets, the multiple gene selection procedures are the best.