Toward Exploratory Test-Instance-Centered Diagnosis in High-Dimensional Classification
IEEE Transactions on Knowledge and Data Engineering
Induction of linear decision trees with real-coded genetic algorithms and k-d trees
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
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Multivariate decision trees overcome a representational limitation of univariate decision trees: univariate decision trees are restricted to splits of the instance space that are orthogonal to the features axis. This paper discusses the following issues for constructing multivariate decision trees: representing a multivariate test, including symbolic and numeric features, learning the coefficients of a multivariate test, selecting the features to include in a test, and pruning of multivariate decision trees. We present some new and review some well-known methods for forming multivariate decision trees. The methods are compared across a variety of learning tasks to assess each method''s ability to find concise, accurate decision trees. The results demonstrate that some multivariate methods are more effective than others. In addition, the experiments confirm that allowing multivariate tests improves the accuracy of the resulting decision tree over univariate trees.