Robustification of Naïve Bayes Classifier and Its Application for Microarray Gene Expression Data Analysis

Table 2

Performance evaluation by different methods based on simulated dataset 1.

Prediction methods	NBC	SVM	KNN	AdaBoost	Proposed	value
Accuracy	0.55	0.84	0.86	0.82	0.97	0.00
95% CI of accuracy	(0.45, 0.65)	(0.75, 0.90)	(0.77, 0.92)	(0.73, 0.89)	(0.91, 0.99)	—
Sensitivity	0.54	0.78	0.79	0.90	0.95	0.00
Specificity	0.62	0.94	0.97	0.76	0.94	0.00
PPV	0.88	0.96	0.98	0.73	0.94	0.00
NPV	0.20	0.71	0.73	0.91	0.94	0.00
Prevalence	0.84	0.63	0.63	0.41	0.40	0.00
Detection rate	0.45	0.49	0.50	0.37	0.48	0.00
Detection prevalence	0.51	0.51	0.51	0.51	0.51	—
MCC	0.12	0.70	0.74	0.65	0.94	0.00
MER	0.49	0.18	0.17	0.08	0.03	0.03