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Table 6 Experiment 2: Classification Accuracy with Random Forest

From: Supervised Regularized Canonical Correlation Analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery

Dataset (Ï•P, Ï•J)

PCA

PLSR

SRCCA TT

SRCCA WRST

SRCCA WLT

(Ï•P, Ï•M)

64%

75%

83%

81%

84%

(Ï•P, Ï•A)

50%

64%

81%

77%

83%

(Ï•P, Ï•H)

64%

67%

91%

89%

93%

  1. Classification accuracies obtained for fusing (Ï•P, Ï•M), (Ï•P, Ï•A), and (Ï•P, Ï•H), with CCA, RCCA, SRCCA TT , SRCCA WRST , and SRCCA WLT using the top d = 3 components, using Ï•RFwith 50 trees and leave-one-out validation to identify patients at the risk of biochemical recurrence from those who are not.
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BMC Bioinformatics

ISSN: 1471-2105

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