A VFDT algorithm optimization and application thereof in data stream classification

Abstract

Data stream mining is a new technology for dynamically extracting feature patterns from data streams. Its core technology is data stream mining algorithms. Data stream mining algorithms are divided into clustering and classification algorithms. In data stream classification algorithms, VFDT (The Concept-adapting Very Fast Decision Tree algorithm is an effective classification decision tree algorithm. The algorithm dynamically constructs a decision tree based on the improvement of the Hoefdding tree. With the inflow of data, new branches are continuously added and outdated Branch. However, the algorithm has the problem of concept drift, and some nodes may no longer meet the Hoefdding bound, which affects the classification mining effect. This paper proposes an optimization algorithm (VFDT), which reduces the impact of concept drift and network noise by adding sliding window technology and fuzzy technology to the VFDT classification mining algorithm. Experimental results show that the algorithm can effectively improve the accuracy of stream data classification mining. Reduce classification errors.