---
title: "Stacked Generalizations: When Does It Work?"
authors:
  - "Kai Ming Ting"
  - "Ian H. Witten"
doi: ""
url: "https://ijcai.org/proceedings/1997/125"
pdf: "https://ijcai.org/proceedings/1997/0125.pdf"
venue: "Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence"
year: 1997
pages: "866-872"
track: "Learning"
keywords:
---

# Stacked Generalizations: When Does It Work?

Kai Ming Ting, Ian H. Witten

*Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence*, pages 866-872.

## Abstract

Stacked generalization is a general method of using a high-level model to combine lowerlevel models to achieve greater predictive accuracy. In this paper we address two crucial issues which have been considered to be a 'black art' in classification tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. We demonstrate the effectiveness of stacked generalization for combining three different types of learning algorithms.