基于广义信息论的决策森林多重子模型集成方法

Multiple Subclassifier Integration Method of Decision Forest Based on General Information Theory

为提高传统决策树学习方法的扩展性和自适应性,基于广义信息论提出决策森林多重子模型集成方法.采用从下至顶的学习策略,将离散化处理和决策树的逻辑表达有机结合在一起,整个学习过程不需要任何人为参与,能自动确定子树数目和子树结构.在UCI机器学习数据集上的实验结果和样例分析验证了本文方法的可行性和有效性.

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.