Prediction of Defective Software Modules Using Class Imbalance Learning

Table 3

Comparison on the basis of sensitivity values on eight datasets.

Dataset	SVM	CBNN	NB	RF	LR	-NN	BBN	C4.5	LSTSVM	WLSTSVM
CM1	0.1511	0.5904	0.4402	0.3228	0.2806	0.3843	0.4582	0.2634	0.7600	0.7750
KC1	0.1986	0.6836	0.3088	0.3161	0.4485	0.4565	0.3359	0.4285	0.7701	0.6400
PC1	0.6624	0.5411	0.3566	0.4182	0.3194	0.2968	0.5591	0.3904	0.5387	0.6791
PC3	0.6385	0.6055	0.2854	0.2250	0.2737	0.4198	0.3172	0.3826	0.6563	0.5938
PC4	0.7296	0.6572	0.3855	0.5451	0.4742	0.4627	0.5802	0.5128	0.7601	0.7846
MC2	0.5208	0.7812	0.3462	0.5569	0.3603	0.4005	0.4300	0.5546	0.7633	0.7867
KC2	0.6924	0.6256	0.5804	0.5613	0.5995	0.6859	0.6450	0.5832	0.7569	0.7804
KC3	0.3347	0.5072	0.4602	0.4374	0.2813	0.5938	0.5742	0.4055	0.7700	0.7500