Ensemble learning for hierarchies of locally arranged models
We propose an ensemble technique to train multiple
individual models for supervised learning tasks. The new
method divides the input space into local regions which are
modelled as a set of hyper-ellipsoids. For each local region
an individual model is trained to approximate or classify data
efficiently. The idea is to use locality in the input space as an
useful constraint to realize diversity in an ensemble. The method
automatically determines the size of the ensemble, realises
an outlier detection mechanism and shows superiority over
comparable methods in a benchmark test. Also, the method was
extended to a hierarchical framework allowing a user to solve
complex learning tasks by combining different sub-solutions
and information sources.