  As more data are produced each day, and faster, data stream mining is growing
in importance, making clear the need for algorithms able to fast process these
data. Data stream mining algorithms are meant to be solutions to extract
knowledge online, specially tailored from continuous data problem. Many of the
current algorithms for data stream mining have high processing and memory
costs. Often, the higher the predictive performance, the higher these costs. To
increase predictive performance without largely increasing memory and time
costs, this paper introduces a novel algorithm, named Online Local Boosting
(OLBoost), which can be combined into online decision tree algorithms to
improve their predictive performance without modifying the structure of the
induced decision trees. For such, OLBoost applies a boosting to small separate
regions of the instances space. Experimental results presented in this paper
show that by using OLBoost the online learning decision tree algorithms can
significantly improve their predictive performance. Additionally, it can make
smaller trees perform as good or better than larger trees.
