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Generalized Unified Decomposition of Ensemble Loss

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AI 2006: Advances in Artificial Intelligence (AI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4304))

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Abstract

Goebel et al. [4] presented a unified decomposition of ensemble loss for explaining ensemble performance. They considered democratic voting schemes with uniform weights, where the various base classifiers each can vote for a single class once only. In this article, we generalize their decomposition to cover weighted, probabilistic voting schemes and non-uniform (progressive) voting schemes. Empirical results suggest that democratic voting schemes can be outperformed by probabilistic and progressive voting schemes. This makes the generalization worth exploring and we show how to use the generalization to analyze ensemble loss.

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References

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© 2006 Springer-Verlag Berlin Heidelberg

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Bouckaert, R.R., Goebel, M., Riddle, P. (2006). Generalized Unified Decomposition of Ensemble Loss. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_136

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  • DOI: https://doi.org/10.1007/11941439_136

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49787-5

  • Online ISBN: 978-3-540-49788-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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