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

Table 2

Recall comparison.

Algorithms	Text type
	Economics	Politics	Sports	Weather	Entertainment	Culture
AdaBoost	0.852	0.857	0.849	0.863	0.859	0.861
AdaBoost.M1	0.852	0.864	0.863	0.877	0.862	0.865
AdaBoost.MR	0.858	0.863	0.849	0.866	0.851	0.867
AdaBoost.ECC	0.851	0.844	0.845	0.850	0.846	0.849
Naïve Bayes	0.761	0.798	0.782	0.804	0.817	0.805
SVM	0.868	0.865	0.874	0.874	0.867	0.876
Neural network	0.834	0.809	0.823	0.811	0.825	0.807
Decision tree	0.815	0.798	0.784	0.819	0.799	0.813
AG𝑘NN DIWC-1	0.897	0.888	0.890	0.913	0.885	0.905
AG𝑘NN DIWC-2	0.909	0.914	0.914	0.922	0.891	0.908
AG𝑘NN DIWC-3	0.921	0.917	0.919	0.923	0.911	0.916