Staged Online Learning: A new approach to classification in high speed data streams

In this research we present a new framework and associated algorithms for mining high speed data streams that take advantage of concept recurrence. Different from previous work our approach detects volatility in a stream and then matches the learning paradigm to the degree of volatility. In high volatility stream segments a decision forest is used as the learning mechanism whereas in low volatility segments an approach driven by the use of stored Fourier spectra are used for learning. Our empirical results show significant processing and memory advantages over an approach that does not take advantage of volatility in the stream.