IVFDT: An Improved Very Fast Decision Tree Approach for Blockchain Data Stream Classification

This chapter delves into the application of the Improved Very Fast Decision Tree (IVFDT) method for classifying data streams in blockchain technologies. The focus is on real-time classification and anomaly detection, which are crucial for enhancing security and fraud detection capabilities in blockchain networks. The text explores the adaptability of IVFDT to dynamic data distributions, its superior performance compared to traditional machine learning techniques, and its potential for real-time transaction classification. Additionally, the chapter provides a comprehensive evaluation of the IVFDT approach, highlighting its efficiency and effectiveness in handling high-speed, constant data streams. The results demonstrate that IVFDT outperforms traditional methods in terms of accuracy, computational cost, and resource usage, making it a feasible alternative for blockchain systems where urgent actions are required. The chapter concludes by discussing the potential future developments and optimizations of the IVFDT algorithm for handling complex big data streams in blockchain environments.