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Classification on Liver ultrasound dataset (10% train, 90% test)

78.5Accuracy

ARMA-C3

Updated 8d ago

Evaluation Results

Method	Links
ARMA-C3 2026.05		78.5	81.8	86.6	84.1
ARMA 2026.05		77.1	80.6	85.7	84
ARMA 2026.05		74.5	82.4	80.1	81.1
SVC 2026.05		74.2	76.7	89.7	82.6
ARMA 2026.05		74.2	79.1	85.1	81.8
APPNP 2026.05		73.7	78.4	85.2	81.6
ARMA 2026.05		73.7	79.2	83.7	81.3
Random Forest 2026.05		73.5	75.1	92.1	82.7
ARMA 2026.05		73.4	78.9	83.7	81.1
GAT 2026.05		73.4	81.6	79.4	80.3
MLPClassifier 2026.05		73	81.7	78.2	79.9
ARMA 2026.05		72.9	84.1	74.9	79
JacobiConv 2026.05		72.6	79.6	81.3	80
GAT 2026.05		72.6	80.7	79	79.6
XGBoost 2026.05		71.5	76	85.5	80.4
GAT 2026.05		71.2	84.7	69.3	76
GAT 2026.05		71	80.8	76.1	78.1
GAT 2026.05		70.8	82	70.4	75.2
GAT 2026.05		70.4	78.3	78.8	78.4
GAT 2026.05		70.2	78.1	76.7	77.3
ARMA-C3 2026.05		0.785	0.818	0.866	0.841
ARMA 2026.05		0.771	0.806	0.857	0.84
ARMA 2026.05		0.745	0.824	0.801	0.811
SVC 2026.05		0.742	0.767	0.897	0.826
ARMA 2026.05		0.742	0.791	0.851	0.818
APPNP 2026.05		0.737	0.784	0.852	0.816
ARMA 2026.05		0.737	0.792	0.837	0.813
Random Forest 2026.05		0.735	0.751	0.921	0.827
ARMA 2026.05		0.734	0.789	0.837	0.811
GAT 2026.05		0.734	0.816	0.794	0.803
MLPClassifier 2026.05		0.73	0.817	0.782	0.799
ARMA 2026.05		0.729	0.841	0.749	0.79
JacobiConv 2026.05		0.726	0.796	0.813	0.8
GAT 2026.05		0.726	0.807	0.79	0.796
XGBoost 2026.05		0.715	0.76	0.855	0.804
GAT 2026.05		0.712	0.847	0.693	0.76
GAT 2026.05		0.71	0.808	0.761	0.781
GAT 2026.05		0.708	0.82	0.704	0.752
GAT 2026.05		0.704	0.783	0.788	0.784
GAT 2026.05		0.702	0.781	0.767	0.773