Accurate molecular classification of cancer using simple rules

BMC Medical Genomics

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

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

^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.

ISSN: 1755-8794