new method for Decision tree based Discernibility matrix and Degree of consistent dependence
Rough set theory is a popular mathematical knowledge to resolve problems which are vagueness and uncertainly.And it has been used of solving the redundancy of attribute and data.Decision tree has been widely used in data mining techniques,because it is efficient,fast and easy to be understood in terms of data classification.There are many approaches have been used to generate a decision tree.In this paper,a novel and effective algorithm is introduced for decision tree.This algorithm is based on the core of discernibility matrix on rough set theory and the degree of consistent dependence.This algorithm is to improve the decision tree on node selection.This approach reduces the time complexity of the decision tree production and space complexity compared with ID3.In the end of the article,there is an example of the algorithm can exhibit superiority.
rough set,ID3,decision tree,information gain,discernibility matrix
Liu Liu Baosheng Wang Qiuxi Zhong Hailiang Hu
Department of Computer, National University of Defense Technology, Changsha 410073, China
国际会议
重庆
英文
390-394
2015-03-21（万方平台首次上网日期，不代表论文的发表时间）