abstract = "The paper presents an adaptive GP boosting ensemble
method for the classification of distributed
homogeneous streaming data that comes from multiple
locations. The approach is able to handle concept drift
via change detection by employing a change detection
strategy, based on self-similarity of the ensemble
behaviour, and measured by its fractal dimension. It is
efficient since each node of the network works with its
local streaming data, and communicate only the local
model computed with the other peer-nodes. Furthermore,
once the ensemble has been built, it is used to predict
the class membership of new streams of data until
concept drift is detected. Only in such a case the
algorithm is executed to generate a new set of
classifiers to update the current ensemble.
Experimental results on a synthetic and real life data
set showed the validity of the approach in maintaining
an accurate and up-to-date GP ensemble.",
notes = "GECCO-2007 A joint meeting of the sixteenth
international conference on genetic algorithms
(ICGA-2007) and the twelfth annual genetic programming
conference (GP-2007).