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Table 5 The results of mean effectiveness on miRNA Sequencing (top 10)

From: A feature selection method based on multiple kernel learning with expression profiles of different types

Methods

SVM-RFE

SVM-RCE

mRMR

IMRelief

SlimPLS

OSFS

FGM

SMKL-FS

KIDNEY

0.922

0.832

0.987

0.901

0.896

0.893

0.916

0.994

BRCA

0.839

0.963

0.979

0.817

0.973

0.893

0.953

0.990

LUNG

0.891

0.946

0.979

0.953

0.831

0.945

0.946

0.980

HNSC

0.979

0.955

0.991

0.879

0.874

0.920

0.874

0.994

LIHC

0.906

0.836

0.911

0.813

0.871

0.789

0.925

0.917

PRAD

0.897

0.933

0.930

0.892

0.905

0.794

0.836

0.946

STAD

0.855

0.870

0.853

0.790

0.823

0.760

0.827

0.880

THCA

0.925

0.901

0.969

0.842

0.876

0.878

0.928

0.967

Mean

0.902

0.904

0.950

0.861

0.881

0.859

0.901

0.958