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Table 3 Logistic Regression Results: One-Versus-One Classification. ROC-AUC scores for one-versus-one logistic regression experiments for all category combinations. Feature sets consist of the number of revisions (nrev), control variables (CV), network metrics (NM), label frequencies (LF), and label transition probabilities (LT). Utilizing all feature sets performs best for all experiments

From: Relating Wikipedia article quality to edit behavior and link structure

Features

ROC-AUC

 

LQ vs HQ

LQ vs CI

LQ vs EW

HQ vs CI

HQ vs EW

CI vs EW

nrev

0.577

0.870

0.968

0.849

0.961

0.810

CV

0.652

0.923

0.989

0.883

0.978

0.837

NM

0.707

0.861

0.938

0.871

0.937

0.710

LF

0.640

0.807

0.880

0.881

0.928

0.665

LT

0.713

0.825

0.907

0.884

0.933

0.740

CV + NM

0.727

0.930

0.987

0.919

0.978

0.833

CV + LF

0.677

0.947

0.992

0.951

0.991

0.850

CV + LT

0.736

0.952

0.994

0.947

0.992

0.874

NM + LF

0.742

0.906

0.967

0.950

0.980

0.738

NM + LT

0.775

0.923

0.974

0.949

0.978

0.788

LF + LT

0.744

0.892

0.956

0.953

0.980

0.769

NM + LF + LT

0.799

0.947

0.986

0.979

0.993

0.813

CV + NM + LF

0.755

0.954

0.991

0.967

0.992

0.840

CV + NM + LT

0.783

0.959

0.994

0.963

0.993

0.863

CV + NM + LF + LT

0.805

0.973

0.996

0.984

0.997

0.879