Constructing Decision Trees for Mining High-speed Data Streams

作     者：Wenhua Xu Zheng Qin 

作者机构：Department of Computer Science and Technology Tsinghua University Beijing China School of Software Tsinghua University Beijing China 

出 版 物：《Chinese Journal of Electronics》 

年 卷 期：2023年第21卷第2期

页      面：215-220页

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：National Natural Science Foundation of China 

主　　题：Training Clustering algorithms Programmable logic arrays Prediction algorithms Classification algorithms Decision trees Data mining 

摘      要：Very fast decision tree is one of the most successful and prominent algorithms specifically designed for stream data classification. In this paper, we develop a new decision tree induction model CFDT (Clustering feature decision tree model), which is an extension to VFDT (Very fast decision tree). CFDT applies a micro-clustering algorithm that scans the data only once to provide the statistical summaries of the data for incremental decision tree induction. Moreover, micro-clusters also serve as classifiers in tree leaves to improve classification accuracy and reinforce any-time property. Our experiments on synthetic and real-world datasets show that CFDT is highly scalable for data streams while also generating high classification accuracy with high speed.