Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles

BMC Bioinformatics

Table 6 Classification results using features selected by genetic algorithm.

Algorithm	Accuracy(%)	TP rate	FP rate	TN rate	FN rate	Sensitivity	Specificity	Precision	Fmeasure	RMSE
C4.5	0.5944	0.61	0.43	0.58	0.39	0.61	0.58	0.64	0.62	0.5718
Random Forest	0.6000	0.71	0.54	0.46	0.29	0.71	0.46	0.63	0.66	0.5047
Bagging	0.6111	0.64	0.43	0.58	0.36	0.64	0.58	0.66	0.65	0.4965
Logitboost	0.6167	0.68	0.46	0.54	0.32	0.68	0.54	0.65	0.66	0.5153
Stacking	0.6056	0.66	0.46	0.54	0.34	0.66	0.54	0.65	0.65	0.4892
Adaboost	0.6167	0.67	0.45	0.55	0.33	0.67	0.55	0.65	0.65	0.5960
Multiboost	0.6111	0.68	0.48	0.53	0.32	0.68	0.53	0.65	0.66	0.6147
Logistic	0.6056	0.67	0.48	0.53	0.33	0.67	0.53	0.63	0.65	0.5122
Naivebayes	0.6000	0.76	0.60	0.40	0.24	0.76	0.40	0.62	0.67	0.5251
Bayesnet	0.5611	0.73	0.65	0.35	0.27	0.73	0.35	0.59	0.65	0.5110
Neural Network	0.5944	0.61	0.43	0.58	0.39	0.61	0.58	0.65	0.62	0.5814
RBFnet	0.6000	0.69	0.51	0.49	0.31	0.69	0.49	0.63	0.65	0.5038
SVM	0.6333	0.72	0.48	0.53	0.28	0.72	0.53	0.66	0.68	0.5985

ISSN: 1471-2105