Journal of Biological Engineering

Table 9 Results summary for the WDBC dataset

From: RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data

Dataset: WDBC
Classifier	Metrics
Classifier	Model	Test Acc	Precision	Recall	F1	Kappa	MCC	GM
GNB	Original	0.9386	0.973	0.8571	0.9114	0.8647	0.8688	0.9194
	RN-SMOTE	0.9386	0.973	0.8571	0.9114	0.8647	0.8688	0.9194
	Extracted	0.9298	0.925	0.881	0.9024	0.8477	0.8483	0.9188
	RN-Extracted	0.9386	0.9268	0.9048	0.9157	0.8674	0.8676	0.9312
SVM-Linear	Original	0.9825	1	0.9524	0.9756	0.9619	0.9626	0.9759
	RN-SMOTE	0.9912	0.9767	1	0.9882	0.9812	0.9814	0.993
	Extracted	0.9825	1	0.9524	0.9756	0.9619	0.9626	0.9759
	RN-Extracted	0.9825	0.9545	1	0.9767	0.9627	0.9633	0.986
XGBOOST	Original	0.9649	1	0.9048	0.95	0.9231	0.9258	0.9512
	RN-SMOTE	0.9737	0.9756	0.9524	0.9639	0.9432	0.9433	0.9691
	Extracted	0.9649	0.9524	0.9524	0.9524	0.9246	0.9246	0.9623
	RN-Extracted	0.9737	0.9535	0.9762	0.9647	0.9437	0.9439	0.9742
LDA	Original	0.9649	1	0.9048	0.95	0.9231	0.9258	0.9512
	RN-SMOTE	0.9561	0.9512	0.9286	0.9398	0.9053	0.9054	0.9501
	Extracted	0.9737	1	0.9286	0.963	0.9426	0.9442	0.9636
	RN-Extracted	1	1	1	1	1	1	1

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