---
title: "A Fusion of Stacking with Dynamic Integration"
authors:
  - "Niall Rooney"
  - "David W. Patterson"
doi: ""
url: "https://ijcai.org/proceedings/2007/457"
pdf: "https://ijcai.org/proceedings/2007/0457.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "2844-2848"
track: ""
keywords:
---

# A Fusion of Stacking with Dynamic Integration

Niall Rooney, David W. Patterson

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 2844-2848.

## Abstract

In this paper we present a novel method that fuses the ensemble meta-techniques of Stack-ing and Dynamic Integration (DI) for regres-sion problems, without adding any major computational overhead. The intention of the technique is to benefit from the varying per-formance of Stacking and DI for different data sets, in order to provide a more robust technique. We detail an empirical analysis of the technique referred to as weighted Meta-Combiner (wMetaComb) and compare its per-formance to Stacking and the DI technique of Dynamic Weighting with Selection. The em-pirical analysis consisted of four sets of ex-periments where each experiment recorded the cross-fold evaluation of each technique for a large number of diverse data sets, where each base model is created using random fea-ture selection and the same base learning al-gorithm. Each experiment differed in terms of the latter base learning algorithm used. We demonstrate that for each evaluation, wMeta-Comb was able to outperform DI and Stack-ing for each experiment and as such fuses the two underlying mechanisms successfully