E-Tree: An Efficient Indexing Structure for Ensemble Models on Data Streams
Knowledge and Data Engineering, IEEE Transactions (2015)
摘要
Ensemble learning is a common tool for data stream classification, mainly because of its inherent advantages of handling large volumes of stream data and concept drifting. Previous studies, to date, have been primarily focused on building accurate ensemble models from stream data. However, a linear scan of a large number of base classifiers in the ensemble during prediction incurs significant costs in response time, preventing ensemble learning from being practical for many real-world time-critical data stream applications, such as Web traffic stream monitoring, spam detection, and intrusion detection. In these applications, data streams usually arrive at a speed of GB/second, and it is necessary to classify each stream record in a timely manner. To address this problem, we propose a novel Ensemble-tree (E-tree for short) indexing structure to organize all base classifiers in an ensemble for fast prediction. On one hand, E-trees treat ensembles as spatial databases and employ an R-tree like height-balanced structure to reduce the expected prediction time from linear to sub-linear complexity. On the other hand, E-trees can be automatically updated by continuously integrating new classifiers and discarding outdated ones, well adapting to new trends and patterns underneath data streams. Theoretical analysis and empirical studies on both synthetic and real-world data streams demonstrate the performance of our approach.
更多查看译文
关键词
intrusion detection,ensemble-tree,r-tree like height-balanced structure,ensemble learning model,spatial indexing,linear complexity,sub-linear complexity,learning (artificial intelligence),web traffic stream monitoring,pattern classification,concept drifting,spam detection,indexing,data stream classification,stream data mining,ensemble learning,spatial database,data mining,classification,e-tree indexing structure,concept drift,market research,data models
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络