Decision tree induction with constrained number of leaf node

• Main Authors: Xiang-Yu Yang, 楊翔宇
• Other Authors: Yan-liang Chen
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/91700158014594986498

碩士 === 國立中央大學 === 資訊管理研究所 === 97 === Classification, which builds a data classification model based on attribute value and label of existing data, is a very widespread data mining technology. Decision tree is one of the most popular classification technologies, because it is easy to understand and has the high efficiency computing. Decision tree is widely applied to signal classification, expert system, and medical diagnosis. Because of the noise data and special case of training data sets, decision tree is always huge and it contains too many branches and rules which are difficult to understand. This shortcoming reduces the availability of decision tree. Therefore, we reduce rules from a decision tree by limiting the number of leaf nodes of the decision tree and achieve the highest accuracy with the number of leaf nodes given by user. For this purpose, we propose a new algorithm. We use the agglomerative approach of the hierarchical clustering to limit the decision tree to binary tree by combining the branches of decision tree. Experiment results show that compared with the C4.5, the proposed algorithm successfully reduces the number of leaf nodes and makes better accuracy.