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 3 The obtained results for MAUC from boosting algorithms using decision tree as a base learner

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
Wine	0.9034	0.9139	0.9783	0.9574	0.9917	0.9659	0.9838	0.9499	0.9621	0.9693	0.9294	0.9164	0.9176	0.932
Wine	± 0.0183	± 0.0128	± 0.0054	± 0.0098	± 0.0025	± 0.0129	± 0.0077	± 0.0167	± 0.0091	± 0.0152	± 0.0168	± 0.0162	± 0.0134	± 0.0139
Hayes-Roth	0.8064	0.7951	0.7588	0.8025	0.8032	0.7979	0.7941	0.7956	0.7734	0.5339	0.8013	0.8107	0.8162	0.8095
Hayes-Roth	± 0.0155	± 0.0104	± 0.0154	± 0.0087	± 0.0138	± 0.0151	± 0.1901	± 0.0153	± 0.0292	± 0.0343	± 0.0107	± 0.0115	± 0.0197	± 0.0173
Contraceptive	0.5281	0.4944	0.5305	0.4904	0.4939	0.5366	0.5032	0.4801	0.4458	0.5005	0.5455	0.3479	0.4422	0.5524
Contraceptive	± 0.0048	± 0.0071	± 0.0046	± 0.0061	± 0.0100	± 0.0072	± 0.0051	± 0.0121	± 0.0074	± 0.0060	± 0.0056	± 0.0044	± 0.0114	± 0.0093
Pen-based	0.9012	0.9581	0.962	0.9554	0.9513	0.9327	0.9726	0.9503	0.9592	0.9526	0.9035	0.9038	0.9029	0.9031
Pen-based	± 0.0055	± 0.0055	± 0.0045	± 0.0053	± 0.0030	± 0.0057	± 0.0037	± 0.0027	± 0.0047	± 0.0020	± 0.0096	± 0.0056	± 0.0061	± 0.0079
Vertebral column	0.7758	0.7758	0.7999	0.7814	0.7794	0.7824	0.7814	0.8069	0.7851	0.7957	0.793	0.7973	0.7911	0.7881
Vertebral column	± 0.0218	± 0.0218	± 0.0230	± 0.0165	± 0.0162	± 0.0155	± 0.0184	± 0.0218	± 0.0169	± 0.0278	± 0.0089	± 0.0135	± 0.0119	± 0.0145
New thyroid	0.8879	0.8713	0.8899	0.9098	0.9063	0.8894	0.6225	0.9071	0.884	0.9097	0.8987	0.8728	0.8855	0.8833
New thyroid	± 0.0217	± 0.0269	± 0.0126	± 0.0206	± 0.0117	± 0.0273	± 0.0217	± 0.0354	± 0.0210	± 0.0109	± 0.0296	± 0.0415	± 0.0431	± 0.0351
Dermatology	0.9551	0.9657	0.9783	0.9681	0.9715	0.9700	0.9187	0.9656	0.9438	0.9024	0.9558	0.9592	0.9517	0.9526
Dermatology	± 0.0103	± 0.0050	± 0.0033	± 0.0087	± 0.0057	± 0.0123	± 0.01691	± 0.0062	± 0.0113	± 0.0121	± 0.0107	± 0.0087	± 0.0101	± 0.0108
Balance scale	0.6131	0.628	0.6719	0.6079	0.6103	0.6204	0.6268	0.6071	0.6618	0.7116	0.3603	0.6086	0.566	0.6893
Balance scale	± 0.0118	± 0.0180	± 0.0246	± 0.0099	± 0.0150	± 0.0091	± 0.0085	± 0.0100	± 0.0209	± 0.0179	± 0.0086	± 0.0064	± 0.0194	± 0.014
Glass	0.6381	0.6644	0.6857	0.7187	0.6455	0.7087	0.6235	0.6662	0.5334	0.4205	0.6407	0.6479	0.5928	0.6412
Glass	± 0.0248	± 0.0324	± 0.0244	± 0.0229	± 0.0485	± 0.0246	± 0.0388	± 0.0192	± 0.0437	± 0.0042	± 0.0324	± 0.0183	± 0.0356	± 0.0169
Heart	0.2839	0.2887	0.3054	0.2673	0.2838	0.2783	0.272	0.3216	0.2949	0.3182	0.2853	0.2781	0.2923	0.298
Heart	± 0.0136	± 0.0112	± 0.0131	± 0.0147	± 0.0171	± 0.0181	± 0.0136	± 0.0202	± 0.0144	± 0.0389	± 0.0152	± 0.0257	± 0.0145	± 0.0165
Car evaluation	0.9848	0.9528	0.985	0.998	0.9851	0.8471	0.9909	0.9589	0.8439	0.9843	0.7711	0.8967	0.9478	0.9698
Car evaluation	± 0.0102	± 0.0115	± 0.0034	± 0.0025	± 0.0075	± 0.0098	± 0.0046	± 0.0086	± 0.0196	± 0.0036	± 0.0278	± 0.0046	± 0.0047	± 0.0049
Thyroid	0.9817	0.9754	0.9889	0.9769	0.9765	0.987	0.9718	0.9867	0.9612	0.8899	0.6667	0.9905	0.9907	0.6667
Thyroid	± 0.0033	± 0.0023	± 0.0026	± 0.0038	± 0.0029	± 0.0022	± 0.0070	± 0.0021	± 0.0168	± 0.0325	± 0.0000	± 0.0022	± 0.0010	± 0.0000
Yeast	0.5088	0.4092	0.4757	0.4958	0.4619	0.539	0.4397	0.4889	0.2946	0.4209	0.4107	0.4806	0.5055	0.4846
Yeast	± 0.0270	± 0.0098	± 0.0139	± 0.0174	± 0.0200	± 0.0159	± 0.0099	± 0.0116	± 0.0133	± 0.0063	± 0.0161	± 0.0153	± 0.0237	± 0.0237
Page blocks	0.8183	0.7858	0.8645	0.8131	0.3038	0.8238	0.8117	0.825	0.8322	0.5566	0.7457	0.7391	0.8895	0.7455
Page blocks	± 0.0149	± 0.0198	± 0.0095	± 0.0173	± 0.0462	± 0.0213	± 0.0188	± 0.0194	± 0.0185	± 0.0274	± 0.0293	± 0.0090	± 0.0058	± 0.0074
Shuttle	0.9479	0.9734	0.9607	0.9608	0.2682	0.9714	0.8121	0.9863	0.9727	0.3947	0.9457	0.9502	0.9467	0.9508
Shuttle	± 0.0177	± 0.0119	± 0.0121	± 0.0213	± 0.0404	± 0.0158	± 0.0149	± 0.0127	± 0.0165	± 0.0317	± 0.0146	± 0.0168	± 0.0116	± 0.0170
Average	0.76896	0.7634	0.789	0.7802	0.6954	0.7767	0.7416	0.7797	0.7432	0.684	0.7102	0.7466	0.7625	0.7511

The best performance is shown in italic for each dataset

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