Multiple Subclassifier Integration Method of Decision Forest Based on General Information Theory
Wang Li · 2009
To improve the scalability and adaptability of traditional decision tree learning algorithm,a novel multiple subclassifier integration method of decision forest is proposed based on general information theory.It adopts down-top learning strategy and combines discretization with logical representation of decision tree naturally.The learning procedure does not require any human intervention.The number and structures of subtrees can be set automatically.Experimental results and instance analysis on UCI machine learning data sets prove the feasibility and effectiveness of the proposed method.