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

Table 1

Precision comparison.

Algorithms	Text type
	Economics	Politics	Sports	Weather	Entertainment	Culture
AdaBoost	0.848	0.855	0.851	0.860	0.851	0.859
AdaBoost.M1	0.857	0.859	0.863	0.847	0.858	0.866
AdaBoost.MR	0.854	0.862	0.847	0.865	0.855	0.862
AdaBoost.ECC	0.848	0.854	0.841	0.843	0.840	0.856
Naïve Bayes	0.769	0.794	0.783	0.806	0.811	0.772
SVM	0.867	0.862	0.870	0.877	0.865	0.871
Neural network	0.832	0.807	0.819	0.824	0.828	0.803
Decision tree	0.809	0.792	0.786	0.831	0.799	0.812
AG𝑘NN DIWC-1	0.887	0.894	0.882	0.911	0.877	0.893
AG𝑘NN DIWC-2	0.899	0.906	0.903	0.921	0.898	0.902
AG𝑘NN DIWC-3	0.918	0.905	0.917	0.924	0.903	0.907