ISSN:
0885-6125
Keywords:
decision trees
;
multivariate tests
;
linear discriminant functions
;
inductive learning
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract Unlike a univariate decision tree, a multivariate decision tree is not restricted to splits of the instance space that are orthogonal to the features' axes. This article addresses several 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 several new methods for forming multivariate decision trees and compare them with several well-known methods. We compare the different methods across a variety of learning tasks, in order to assess each method's ability to find concise, accurate decision trees. The results demonstrate that some multivariate methods are in general more effective than others (in the context of our experimental assumptions). In addition, the experiments confirm that allowing multivariate tests generally improves the accuracy of the resulting decision tree over a univariate tree.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1022607123649
Permalink
