Skip to main content

Advertisement

Table 12 Comparison of best classification accuracy for the Prostate Cancer dataset

From: Accurate molecular classification of cancer using simple rules

Methods (feature selection + classification) #Selected genes #Correctly classified samples (accuracy) Rule-based classifier
depended degree + decision rules [this work] 1 31 (91.18%) yes
  2 27 (79.41%)  
TSP [14] 2 32 (94.12%) yes
PCLs [50] unknown 33 (97.06%) yes
discretization + Single C4.5 [11] unknown 23 (67.65%) yes
discretization + Bagging C4.5 [11] unknown 25 (73.53%) yes
discretization + AdaBoost C4.5 [11] unknown 23 (67.65%) yes
RCBT [13] unknown 33 (97.06%) yes
SVMs [13] unknown 27 (79.41%) no
signal to noise ratios + k-NNs [18]d 4 26 (77.2%) no
  16 29 (85.7%) no
  1. dIn [18], as both raw and normalized datasets were used, two groups of prediction results were obtained. Here, we chose their results from the normalized dataset. Another small difference is that we obtained the dataset from the Kent Ridge Bio-medical Data Set Repository, where the prostate test set includes 25 tumor and 9 normal samples instead of the 27 tumor and 8 normal samples studied in [69]. To facilitate comparison, the correctly classified sample numbers were calculated according to the total of 34 samples.