Stacked Generalizations: When Does It Work?

Ting, Kai Ming; Witten, Ian H.

Stacked Generalizations: When Does It Work?

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Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence. Learning. 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.

Kai Ming Ting, and Ian H. Witten. 1997. Stacked Generalizations: When Does It Work?. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, IJCAI-97. IJCAI, 866–872. https://ijcai.org/proceedings/1997/125

@inproceedings{ijcai1997p125,
  title     = {Stacked Generalizations: When Does It Work?},
  author    = {Ting, Kai Ming and Witten, Ian H.},
  booktitle = {Proceedings of the Fifteenth International Joint Conference on
               Artificial Intelligence, {IJCAI-97}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Martha E. Pollack},
  pages     = {866--872},
  year      = {1997},
  note      = {Learning},
  url       = {https://ijcai.org/proceedings/1997/125},
}

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TI  - Stacked Generalizations: When Does It Work?
AU  - Ting, Kai Ming
AU  - Witten, Ian H.
T2  - Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, IJCAI-97
PB  - International Joint Conferences on Artificial Intelligence
PY  - 1997
SP  - 866
EP  - 872
UR  - https://ijcai.org/proceedings/1997/125
ER  -