Non-strict heterogeneous stacking

doi:10.1016/j.patrec.2007.01.018

Return

Non-strict heterogeneous stacking

2007-07-01

PRE

Niall Rooney *

David Patterson

Chris Nugent

DOI：10.1016/j.patrec.2007.01.018

Original

Original request for help

Save

Abstract

En 中文

In this paper, we evaluate a new ensemble schema for regression, where the ensemble is composed of a number of models where each model is built using feature sampled data using a learning algorithm drawn from a set of simple and stable learning algorithms, and the ensemble integration method is Stacking. We evaluate this schema referred to as non-strict heterogeneous Stacking to a number of baseline methods and to strict heterogeneous Stacking, which uses the same number of models as there are base learning algorithms, built using un-sampled data. We demonstrate that non-strict Stacking for the set of base learning algorithms evaluated, strongly outperformed the baseline methods. In addition the added flexibility of non-strict Stacking, allowed it both to outperform strict Stacking and homogeneous Stacking for the same set of base learning algorithms considered. We discuss the conditions in general where non-strict heterogeneous Stacking is likely to be advantageous. (c) 2007 Elsevier B.V. All rights reserved.

Keywords:

ensemble learning

meta learning

regression

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.