Table 3.

Performance comparison of support vector machine (SVM)-based ensemble method with SVM-based other classifiers on benchmarking dataset.

Method	Matthews’ correlation coefficient (MCC)	Accuracy	Sensitivity	Specificity	AUC	P-value
Amino acid composition (AAC)	0.219 ± 0.024	0.609 ± 0.012	0.645 ± 0.023	0.573 ± 0.019	0.641 ± 0.016	0.006
Dipeptide composition (DPC)	0.269 ± 0.018	0.635 ± 0.009	0.635 ± 0.012	0.634 ± 0.016	0.683 ± 0.009	0.491
Composition–transition–distribution (CTD)	0.182 ± 0.030	0.591 ± 0.015	0.579 ± 0.019	0.603 ± 0.020	0.621 ± 0.016	0.0003
Physicochemical properties (PCP)	0.172 ± 0.020	0.585 ± 0.010	0.523 ± 0.035	0.648 ± 0.027	0.620 ± 0.012	0.0002
Amino acid index (AAI)	0.228 ± 0.015	0.613 ± 0.007	0.650 ± 0.018	0.577 ± 0.017	0.642 ± 0.010	0.008
Hybrid	0.218 ± 0.020	0.609 ± 0.010	0.602 ± 0.014	0.616 ± 0.018	0.647 ± 0.013	0.015
Ensemble learning (EL)	0.298 ± 0.022	0.649 ± 0.011	0.618 ± 0.018	0.679 ± 0.009	0.697 ± 0.011	–

The first column corresponds to the performance of individual feature group, hybrid feature, and ensemble learning. The column 2–6 respectively represent the MCC, accuracy, sensitivity, specificity, and AUC value, where each value shown as the average ± SD of 10 alternative balanced datasets. The last column represents a pairwise comparison of AUC between EL and the other methods using a two-tailed t-test. P ≤ 0.05 indicates a statistically meaningful difference between EL and the selected composition (shown in bold).