Boosting methods for multi-class imbalanced data classification: an experimental review

Tanha, Jafar; Abdi, Yousef; Samadi, Negin; Razzaghi, Nazila; Asadpour, Mohammad

doi:10.1186/s40537-020-00349-y

Journal of Big Data

Table 19 The obtained results for evaluation metrics from running boosting algorithms on multi-class imbalanced big datasets

From: Boosting methods for multi-class imbalanced data classification: an experimental review

Dataset	Algorithm
Dataset	AdaBoost.MH	SAMME	CatBoost	LogitBoost	GradientBoost	XGBoost	MEBoost	SMOTEBoost	RUSBoost	LightGBM	AdaC1	AdaC2	AdaC3	AdaCost
FARS
MAUC	0.5382	0.4754	0.5330	0.5428	0.2112	0.5108	0.5543	0.4546	0.3708	0.4755	0.5970	0.3327	0.5190	0.4306
MAUC	± 0.0022	± 0.0129	± 0.0030	± 0.0039	± 0.2228	± 0.0029	± 0.0029	± 0.0153	± 0.0195	± 0.0089	± 0.0117	0.0138±	± 0.0093	± 0.130
MMCC	0.1803	0.0907	0.1772	0.1962	− 0.3315	0.1384	0.2078	0.0580	− 0.1258	0.1084	0.2107	0.4295	0.3771	− 0.1482
MMCC	± 0.0027	± 0.0134	± 0.0043	± 0.0052	± 0.0401	± 0.0037	± 0.0034	± 0.0207	± 0.0202	± 0.0166	± 0.0070	0.0261±	± 0.0032	± 0.0243
G-mean	0.0000	0.0000	0.0000	0.0000	0.0031	0.0000	0.0000	0.0000	0.0368	0.0000	0.1013	0.0000	0.1215	0.0608
G-mean	± 0.0000	± 0.0000	± 0.0000	± 0.0000	± 0.0072	± 0.0000	± 0.0000	± 0.0000	± 0.0422	± 0.0000	± 0.0700	± 0.0000	± 0.0579	± 0.0159
KDD Cup’99
MAUC	0.9251	0.9267	0.9333	0.9637	0.3057	0.8755	0.9468	0.9485	0.7874	0.4601	0.8380	0.8553	0.9440	0.6618
MAUC	± 0.0158	± 0.0109	± 0.0155	± 0.0112	± 0.0218	± 0.0078	± 0.0036	± 0.0172	± 0.0294	± 0.0274	± 0.0169	± 0.0250	± 0.0042	± 0.0468
MMCC	0.8523	0.8320	0.8106	0.9119	0.0236	0.7120	0.8873	0.8122	0.3392	0.0171	0.3678	0.4477	0.6201	0.2050
MMCC	± 0.0000	± 0.0124	± 0.0180	± 0.0161	± 0.0312	± 0.0109	± 0.0066	± 0.0268	± 0.0423	± 0.0496	± 0.0186	± 0.0309	± 0.0090	± 0.0428
G-mean	0.9398	0.9411	0.0000	0.9728	0.0649	0.0000	0.9535	0.8696	0.5072	0.0000	0.2637	0.3621	0.6894	0.0564
G-mean	± 0.0156	± 0.0055	± 0.0000	± 0.0101	± 0.0179	± 0.0000	± 0.0058	± 0.0292	± 0.0375	± 0.0000	± 0.0294	± 0.0431	± 0.0124	± 0.0222
Covtype
MAUC	0.6533	0.4085	0.6624	0.8367	0.7003	0.5732	0.7722	0.4737	0.4002	0.7886	0.7416	0.4301	0.6978	0.6379
MAUC	± 0.0000	± 0.0033	± 0.0026	0.0022	± 0.9982	± 0.0008	± 00.002	± 0.0101	± 0.0172	± 0.0120	± 0.0014	± 0.0000	± 0.0006	± 0.0020
MMCC	0.2046	− 0.1357	0.2276	0.5189	0.2874	0.0791	0.3963	0.0757	− 0.1335	0.4593	0.3395	− 0.1344	0.2995	0.2146
MMCC	± 0.0012	± 0.0052	± 00.004	± 0.0033	± 0.0117	± 0.0012	± 0.0032	± 0.0159	± 0.0247	± 0.0151	± 0.0023	± 0.0035	± 0.0010	± 0.0030
G-mean	0.6839	0.3885	0.6967	0.8594	0.7003	0.6038	00.796	0.7237	0.3313	0.8138	0.5551	0.0000	0.5033	0.0666
G-mean	± 0.0018	± 0.0156	± 0.0033	± 0.0024	± 0.0082	± 0.0020	± 0.0013	± 0.0089	± 0.0132	± 0.0154	± 0.0027	± 0.0000	± 0.0009	± 0.0544
Poker
MAUC	0.0524	0.1557	0.2376	0.1765	0.1168	0.1350	0.2585	0.1659	0.1117	0.2593	0.2217	0.09357	0.2087	0.06224
MAUC	± 0.0014	± 0.0073	± 0.0042	± 0.0005	± 0.0116	± 0.0023	± 0.0201	± 0.0018	± 0.0045	± 0.0157	± 0.0041	± 0.0136	± 0.0162	± 0.0216
MMCC	− 0.4545	− 0.3696	− 0.3011	− 0.3889	− 0.3556	− 0.5599	0.2879−	− 0.2821	− 0.3352	0.275–0	− 0.3790	− 0.8569	− 0.2284	− 0.4425
MMCC	± 0.0035	± 0.0140	± 0.0038	± 0.0126	± 0.0083	± 0.0082	± 0.0162	± 0.0076	± 0.0086	± 0.0241	± 0.0089	± 0.0272	± 0.0098	± 0.0155
G-mean	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0018	0.0000
G-mean	± 0.0000	± 0.0000	± 0.0000	± 0.0000	± 0.0000	± 0.0000	± 0.0000	± 0.0000	± 0.0000	± 0.0000	± 0.0000	± 0.0000	± 0.0040	± 0.0000
Average over MMCC	0.1957	0.1044	0.2286	0.3095	− 0.0940	0.0924	0.3009	0.1660	− 0.0638	0.0775	0.1348	− 0.0285	0.2671	− 0.0428

The best performance is shown in italic for each dataset

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