Learning with Subset Stacking

• URL: http://arxiv.org/abs/2112.06251v3
• Date: Mon, 30 Oct 2023 12:56:34 GMT
• Title: Learning with Subset Stacking
• Authors: S. \.Ilker Birbil, Sinan Yildirim, Kaya G\"okalp, M. Hakan Aky\"uz
• Abstract summary: We propose a new regression algorithm that learns from a set of input-output pairs. We call this algorithm LEarning with Subset Stacking'' or LESS, due to its resemblance to the method of stacking regressors.
• Score: 0.40964539027092906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a new regression algorithm 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.

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