Abstract：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.
王利民，徐沛娟，李雄飞. 基于广义信息论的决策森林多重子模型集成方法*[J]. 模式识别与人工智能, 2009, 22(2): 325-329.
WANG Li-Min, XU Pei-Juan, LI Xiong-Fei. Multiple Subclassifier Integration Method of Decision Forest Based on General Information Theory. , 2009, 22(2): 325-329.
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2009, Vol. 22 
