A Novel Approach of Feature Space Reconstruction with Three-Way Decisions for Long-Tailed Text Classification

Table 4

The performance results of our proposed model and baseline methods.

Method	Datatype	Accuracy	Macroprecision	Macrorecall	Macro-F1 score
TF-IDF	All data	0.79	0.65	0.635	0.64
	Head class	0.872	0.854	0.857	0.857
	Tail class	0.635	0.597	0.581	0.585
CNN	All data	0.915	0.765	0.732	0.747
	Head class	0.943	0.937	0.928	0.933
	Tail class	0.747	0.731	0.692	0.709
RNN	All data	0.905	0.783	0.767	0.775
	Head class	0.932	0.927	0.916	0.918
	Tail class	0.782	0.759	0.728	0.742
BI-LSTM	All data	0.927	0.82	0.796	0.81
	Head class	0.954	0.947	0.94	0.943
	Tail class	0.821	0.778	0.747	0.752
Our method	All data	0.933	0.842	0.825	0.832
	Head class	0.953	0.945	0.951	0.948
	Tail class	0.841	0.813	0.797	0.801

Bold values means the best values in accuracy, macroprecision, macrorecall, and macro-F1 score.