B0640
Title: Learning with subset stacking
Authors: Ilker Birbil - University of Amsterdam (Netherlands)
Sinan YILDIRIM - Sabanci University (Turkey)
Kaya Gokalp - Sabanci University (Turkey)
Hakan Akyuz - Erasmus University Rotterdam (Netherlands) [presenting]
Abstract: A new regression algorithm is proposed that learns from a set of input-output pairs. Our algorithm is designed for populations where the relation between the input variables and the output variable exhibits a heterogeneous behavior across the predictor space. The algorithm starts with generating subsets that are concentrated around random points in the input space. This is followed by training a local predictor for each subset. Those predictors are then combined in a novel way to yield an overall predictor. We call this algorithm ``LEarning with Subset Stacking'' or LESS, due to its resemblance to the method of stacking regressors. We compare the testing performance of LESS with state-of-the-art methods on several datasets. Our comparison shows that LESS is a competitive supervised learning method. Moreover, we observe that LESS is also efficient in terms of computation time and it allows a straightforward parallel implementation.
