Multiclass Boosting with Adaptive Group-Based kNN and Its Application in Text Categorization

Table 3

𝐹₁-measure comparison.

Algorithms	Text type
	Economics	Politics	Sports	Weather	Entertainment	Culture
AdaBoost	0.850	0.856	0.850	0.862	0.855	0.860
AdaBoost.M1	0.855	0.862	0.863	0.876	0.860	0.866
AdaBoost.MR	0.856	0.863	0.848	0.866	0.853	0.865
AdaBoost.ECC	0.849	0.849	0.848	0.847	0.843	0.847
Naïve Bayes	0.765	0.796	0.783	0.805	0.814	0.804
SVM	0.868	0.864	0.872	0.874	0.876	0.864
Neural network	0.833	0.808	0.821	0.808	0.827	0.805
Decision tree	0.812	0.795	0.785	0.825	0.799	0.813
AG𝑘NN DIWC-1	0.896	0.888	0.896	0.912	0.881	0.899
AG𝑘NN DIWC-2	0.895	0.910	0.909	0.922	0.906	0.903
AG𝑘NN DIWC-3	0.907	0.911	0.918	0.924	0.920	0.912