Abstract:
Nowadays, data stream mining is a very hot and high
attention research field due to the real-time industrial
applications from different sources are generating amount of
data continuously as the streaming style. To process these
growing and large data streams, data stream mining,
classification algorithms have been proposed. These
algorithms have to deal with high processing time and
memory costs, class imbalance, overfitting and concept drift
and so on. It is sure that ensembles of classifiers are being
effectively used to make improvement in the accuracy of
single classifiers in either data mining or data stream mining.
Thus, to get higher performance in prediction with largely no
increasing memory and time costs, this paper proposes an
Online Boosting(OLBoost) Approach, which is firstly use the
Extremely Fast Decision Tree (EFDT) as base (weak)
learner , in order to ensemble them into a single online strong
learner. The experiments of the proposed method were
carried out for credit card fraud detection domain with the
sample benchmark datasets.
