关键词: 模糊决策树, 集成分类, 混沌布谷鸟算法, 投票机制, 分类精度

Abstract: When constructing the Fuzzy Decision Tree（FDT）, data need to be fuzzy. The most important thing is to determine the fuzzy language item number for each quantitative attribute. On the other hand, ensemble classification algorithm has become an effective strategy to improve the model’s accuracy and stability. So, a FDT ensemble classification algorithm based on Chaotic Cuckoo Search（CCS） optimization is proposed. Firstly, the model uses CCS algorithm to choose the fuzzy language item number of each quantitative attribute. Secondly, using the bootstrap sampling to generate FDT ensemble model, at last the model gets the classification results by OOB error weighted voting mechanism. Through 4 groups of UCI data sets experiments, it shows that compared with other classification algorithm, the method has significant improvement on classification accuracy. At the same time, in dealing with missing data, there is still a high classification ability.

Key words: Fuzzy Decision Tree（FDT）, ensemble classification, Chaotic Cuckoo Search（CCS） algorithm, voting mechanism, classification accuracy