PRISMA: Improving Risk Estimation with Parallel Logistic Regression Trees

Arnrich B, Albert AA, Walter JA (2006)
In: From Data and Information Analysis to Knowledge Engineering. Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V. University of Magdeburg, March 9–11, 2005. Studies in Classification, Data Analysis, and Knowledge Organization, . Berlin: Springer: 87-94.

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Abstract
Logistic regression is a very powerful method to estimate models with binary response variables. With the previously suggested combination of tree-based approaches with local, piecewise valid logistic regression models in the nodes, interactions between the covariates are directly conveyed by the tree and can be interpreted more easily. We show that the restriction of partitioning the feature space only at the single best attribute limits the overall estimation accuracy. Here we suggest Parallel RecursIve Search at Multiple Attributes (PRISMA) and demonstrate how the method can significantly improve risk estimation models in heart surgery and successfully perform a benchmark on three UCI data sets.
Publishing Year
Conference
29th Annual Conference of the Gesellschaft für Klassifikation e.V.
Location
Magdeburg, Germany
Conference Date
2005-03-09 – 2005-03-11
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Arnrich B, Albert AA, Walter JA. PRISMA: Improving Risk Estimation with Parallel Logistic Regression Trees. In: From Data and Information Analysis to Knowledge Engineering. Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V. University of Magdeburg, March 9–11, 2005. Studies in Classification, Data Analysis, and Knowledge Organization. Berlin: Springer; 2006: 87-94.
Arnrich, B., Albert, A. A., & Walter, J. A. (2006). PRISMA: Improving Risk Estimation with Parallel Logistic Regression Trees. From Data and Information Analysis to Knowledge Engineering. Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V. University of Magdeburg, March 9–11, 2005, 87-94.
Arnrich, B., Albert, A. A., and Walter, J. A. (2006). “PRISMA: Improving Risk Estimation with Parallel Logistic Regression Trees” in From Data and Information Analysis to Knowledge Engineering. Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V. University of Magdeburg, March 9–11, 2005 Studies in Classification, Data Analysis, and Knowledge Organization (Berlin: Springer), 87-94.
Arnrich, B., Albert, A.A., & Walter, J.A., 2006. PRISMA: Improving Risk Estimation with Parallel Logistic Regression Trees. In From Data and Information Analysis to Knowledge Engineering. Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V. University of Magdeburg, March 9–11, 2005. Studies in Classification, Data Analysis, and Knowledge Organization. Berlin: Springer, pp. 87-94.
B. Arnrich, A.A. Albert, and J.A. Walter, “PRISMA: Improving Risk Estimation with Parallel Logistic Regression Trees”, From Data and Information Analysis to Knowledge Engineering. Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V. University of Magdeburg, March 9–11, 2005, Studies in Classification, Data Analysis, and Knowledge Organization, Berlin: Springer, 2006, pp.87-94.
Arnrich, B., Albert, A.A., Walter, J.A.: PRISMA: Improving Risk Estimation with Parallel Logistic Regression Trees. From Data and Information Analysis to Knowledge Engineering. Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V. University of Magdeburg, March 9–11, 2005. Studies in Classification, Data Analysis, and Knowledge Organization. p. 87-94. Springer, Berlin (2006).
Arnrich, Bert, Albert, Alexander A., and Walter, Jörg A. “PRISMA: Improving Risk Estimation with Parallel Logistic Regression Trees”. From Data and Information Analysis to Knowledge Engineering. Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V. University of Magdeburg, March 9–11, 2005. Berlin: Springer, 2006. Studies in Classification, Data Analysis, and Knowledge Organization. 87-94.
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