An effective pattern-based Bayesian classifier for evolving data stream

Abstract

Abstract One of the hot topics in graph-based machine learning is to build Bayesian classifier from large-scale dataset. An advanced approach to Bayesian classification is based on exploited patterns. However, traditional pattern-based Bayesian classifiers cannot adapt to the evolving data stream environment. For that, an effective Pattern-based Bayesian classifier for Data Stream (PBDS) is proposed. First, a data-driven lazy learning strategy is employed to discover local frequent patterns for each test record. Furthermore, we propose a summary data structure for compact representation of data, and to find patterns more efficiently for each class. Greedy search and minimum description length combined with Bayesian network are applied to evaluating extracted patterns. Experimental studies on real-world and synthetic data streams show that PBDS outperforms most state-of-the-art data stream classifiers.