Loading...
Thumbnail Image
Item

Stacked generalization: when does it work?

Abstract
Stacked generalization is a general method of using a high-level model to combine lower-level models to achieve greater predictive accuracy. In this paper we resolve 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, and also for combining models of the same type derived from a single learning algorithm in a multiple-data-batches scenario. We also compare the performance of stacked generalization with published results of arcing and bagging.
Type
Working Paper
Type of thesis
Series
Computer Science Working Papers
Citation
Ting, K.M. & Witten, I.H. (1997). Stacked generalization: when does it work? (Working paper 97/03). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
Date
1997-01
Publisher
Department of Computer Science, University of Waik
Degree
Supervisors
Rights