Table 7

Predictive performance for different models

Data set

Methods

Training set

Test set


Accuracy

MCC

Accuracy

MCC


I

MILESa

0.941

0.881

0.861

0.725

Decision tree

0.915

0.830

0.781

0.569

1-norm SVM

1.000

1.000

0.832

0.668

Random forest

0.995

0.990

0.891

0.783


II

MILESa

0.978

0.956

0.904

0.807

Decision tree

0.955

0.913

0.919

0.837

1-norm SVM

0.980

0.961

0.882

0.765

Random forest

0.945

0.896

0.868

0.754


III

MILESb

0.947

0.885

0.846

0.711

Decision tree

0.966

0.924

0.838

0.682

1-norm SVM

0.995

0.988

0.812

0.624

Random forest

0.982

0.959

0.855

0.717


IV

MILESb

0.898

0.811

0.794

0.584

Decision tree

0.914

0.829

0.698

0.398

1-norm SVM

0.952

0.906

0.714

0.418

Random forest

0.936

0.877

0.698

0.392


a Manhattan dissimilarity measure; b Rogers-Tanimoto dissimilarity measure.

Fu et al. BMC Bioinformatics 2012 13(Suppl 15):S3   doi:10.1186/1471-2105-13-S15-S3

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