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Table 6 Classification results using features selected by genetic algorithm.

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

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