Decision forest learning algorithm based on relational data analysis
LI Xiong-fe · Computer Engineering and Applications Journal · 2008
Multiple classifier integration in pattern recognition has received more attention and becomes one research hot.This paper proposes a multiple submodel integration algorithm based on decision forest construction.By giving distinct classification rule to each sample,decision forest rather than decision tree is constructed to automatically determine relatively independent attribute subset,and based on this we integrate submodel by applying conditional independence assumption.The whole learning procedure do not need any human interference.The independent structure of each subtree and the number of decision trees can be determined,which can help different classifiers to play advantage on different samples and domains.Theory analysis and experimental study on UCI data sets prove its feasibility and effectiveness.