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Table 1 Model Comparison The comparison of optimal performance values and number of latent variables for three independent models on the 36- and 60-month data.

From: Kernelized partial least squares for feature reduction and classification of gene microarray data

(CFR-data) Model Top Validation AUC Value (36 mo/60 mo) Number of Latent Variables (30 mo/60 mo)
L-PLS .791/.831 3/2
KPLS-Poly (Degree = 1) .784/.830 3/1
SVM .78/- -
  1. The best performance was seen with the L-PLS, out-competing the non-linear SVM and KPLS techniques in AUC performance. The number of latent variables required for the PLS-based techniques was no more than three for both data sets.