Algorithm of Constructing Decision Tree Based on Rough Set Theory
Problem of classification is the main research target of many algorithms in machine learning and data mining. Of all the algorithms, decision tree is more preferred by researchers due to its clarity and readability. Attribute of little value domain is the important feature of training dataset of decision trees. Based on this, this paper presents a new approach to construct decision tree after reducing dimension and compressing data set. Experiment shows that the algorithm proposed in this paper improves the efficiency in real applications compared with traditional algorithms.
Decision Trees Rough Set Theory Information Gain Data Compression
Baowei Song Chunxue Wei
School of Computer and Communication Engineering Zheng Zhou University of Light Industry Zheng Zhou School of Light Chemical Engineering Henan Industry Design School Zheng Zhou 450002, China
国际会议
成都
英文
300-302
2010-06-12（万方平台首次上网日期，不代表论文的发表时间）