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Table 4 Comparison of classification accuracy using top 100 features for platform transition.

From: Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping

Feature selection CV (%) SVM-RFE (%) RF_based_FS (%)
Classifier FC Equal-W Equal-F k-means FC Equal-W Equal-F k-means FC Equal-W Equal-F k-means
SVM 40.8 26.3 84.2 81.6 36.8 40.8 85.5 39.5 28.9 30.2 76.3 39.5
RF 67.1 73.7 89.5 76.3 55.2 60.5 86.8 80.2 56.6 81.6 90.8 85.5
NB 25.0 23.7 80.2 71.0 32.9 23.7 76.3 22.3 23.7 23.7 84.2 36.8
PAM 35.5 23.7 78.9 64.5 39.5 27.6 73.7 32.9 39.5 23.7 81.6 44.7
  1. The classification models were trained on exon-array (342 samples) and tested on RNA-seq (76 samples) data. Highest accuracy for each classification method is marked in bold. Only RF with Equal-F binning achieved greater than 90% classification accuracy.