Fuzzy Rough Based Decision Tree Constructing
Decision tree and fuzzy rough set are two distinct but complementary classifiers.Decision tree is a simple and easy-understandable rule-based classifier,whereas the tool of fuzzy rough sets are effective on attribute and sample reduction.It is promising to propose an approach to integrate these two rule based classification tools to construct a novel decision tree based on fuzzy rough set.In this paper,based on the basic concept of fuzzy rough sets,i.e.,consistence degree,we propose a fuzzy rough decision tree which is completely different from the existing classification trees.The three key basic elements of decision tree,i.e.,node,branch and leaf,are designed in a new way by using the notions in fuzzy rough sets.And then one algorithm to build fuzzy rough tree classifier is proposed.Finally,experimental results show that the proposed algorithm is readily comprehensible and effective.
Fuzzy rough sets Decision tree Consistence degree Rule-based classifier
CHEN Wei ZHAO Su-yun CHEN Hong LI Cui-ping
Key Laboratory of Data Engineering and Knowledge Engineering, Renmin University of China,MOE, Beijin Key Laboratory of Data Engineering and Knowledge Engineering, Renmin University of China,MOE, Beijin
国内会议
第十二届中国Rough集与软计算学术会议、第六届中国Web智能学术研讨会及第六届中国粒计算学术研讨会联合学术会议
合肥
英文
14-14
2012-10-13（万方平台首次上网日期，不代表论文的发表时间）