Decision Trees for Probabilistic Data

Authors:
Jean-Pascal Aboa;Richard Emilion
Affiliations:
-;-
Venue:
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Year:
2000

Citing 1
Cited 1

Induction of Decision Trees

Machine Learning

Unsupervised classification and analysis of objects described by nonparametric probability distributions

Statistical Analysis and Data Mining

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Abstract

We propose an algorithm to build decision trees when the observed data are probability distributions. This is of interest when one deals with massive database or with probabilistic models. We illustrate our method with a dataset describing districts of Great Britain. Our decision tree yields rules which explain the unemployment rate. The decision tree in our case is built by replacing the test X α, which is used to split the nodes in the usual case of real numbers, by the test P(X α)