Performance evaluation of GraphM(C), GraphS(C), and 9 well-known discretizers in high dimension data, microarray data problem

Microarray Datasets

1 BreastCancer breastCancer-tra.arff breastCancer-tst.arff
2 Burczynski burczynski-tra.arff burczynski-tst.arff
3 CentralNervousSystem CentralNervousSystem-tra.arff CentralNervousSystem-tst.arff
4 Chiaretti chiaretti-tra.arff chiaretti-tst.arff
5 Chin chin-tra.arff chin-tst.arff
6 Chowdary chowdary-tra.arff chowdary-tst.arff
7 ColonTumor colonTumor-tra.arff colonTumor-tst.arff
8 E2A PBX1 E2A-PBX1-tra.arff E2A-PBX1-tst.arff
9 Gordon gordon-tra.arff gordon-tst.arff
10 Hyperdip50 Hyperdip50-tra.arff Hyperdip50-tst.arff
11 LungCancer Harvard1 LungCancer-Harvard1-tra.arff LungCancer-Harvard1-tst.arff
12 Lungcancer ontario lungcancer-ontario-tra.arff lungcancer-ontario-tst.arff
13 Lung Michigan lung-Michigan-tra.arff lung-Michigan-tst.arff
14 MLL leukemia MLL_leukemia-tra.arff MLL_leukemia-tst.arff
15 Nakayama nakayama-tra.arff nakayama-tst.arff
16 Singh singh-tra.arff singh-tst.arff
17 Sun sun-tra.arff sun-tst.arff
18 TEL AML1 TEL-AML1-tra.arff TEL-AML1-tst.arff
19 Tian tian-tra.arff tian-tst.arff
20 Yeoh yeoh-tra.arff yeoh-tst.arff

Discretization results

Average number of intervals per attribute
Average number of remove attributes per dataset
The execution time per dataset (seconds)

Classification performance results

Predictive Accuracy (ACC)
Area Under the ROC Curve (AUC)
Cohen's Kappa rate (Kappa)

Source code of GraphS and GraphM discretizers

These source codes are implemented in Java, using Netbean IDE, you can download at below.
Disc.rar