关键词: 模式识别, 多重子模型集成, 决策森林, 条件独立性假设

Abstract: 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.

Key words: pattern recognition, multiple submodel integration, decision forest, conditional independence assumption