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by Carla E. Brodley, Paul E. Utgoff
ftp://ftp.cs.umass.edu/pub/techrept/techreport/1992/UM-CS-1992-083.ps
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Abstract:
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 feature's 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
Citations
|
2961
|
Pattern Classification and Scene Analysis
– Duda, Hart
- 1973
|
|
2488
|
Induction of Decision Trees
– Quinlan
- 1986
|
|
2438
|
Classification and Regression Trees
– Breiman, Friedman, et al.
- 1984
|
|
536
|
Learnability and the Vapnik-Chervonenkis Dimension
– Blumer, Ehrenfeucht, et al.
- 1989
|
|
496
|
The Use of Multiple Measurements in Taxonomic Problems
– Fisher
- 1936
|
|
191
|
Boolean feature discovery in empirical learning
– Pagallo, Haussler
- 1990
|
|
151
|
An Empirical Comparison of Pruning Methods for Decision Tree
– Mingers
- 1987
|
|
135
|
An Empirical Comparison of Selection Measures for Decision Tree Induction”, Machine learning
– Mingers
- 1989
|
|
113
|
Learning machines
– Nilsson
- 1965
|
|
111
|
On the handling of continuous-valued attributes in decision tree generation
– Fayyad, Irani
- 1992
|
|
93
|
Unknown Attribute Values in Induction
– Quinlan
- 1989
|
|
88
|
The effect of noise on concept learning
– Quinlan
- 1986
|
|
85
|
A further comparison of splitting rules for decision-tree induction
– Buntine, Niblett
- 1992
|
|
83
|
The Attribute Selection Problem in Decision Tree Generation
– Fayyad, Irani
- 1992
|
|
63
|
Feature Selection and Extraction
– Kittler
- 1986
|
|
56
|
Survey of Decision Tree Classifier Methodology
– Safavian, Landgrebe
- 1991
|
|
37
|
Feature Construction: An Analytic Framework and an Application to Decision Trees
– Matheus
- 1989
|
|
35
|
Optimal linear discriminants
– Gallant
- 1986
|
|
34
|
International application of a new probability algorithm for the diagnosis of coronary artery disease
– Detrano, Janosi, et al.
- 1989
|
|
31
|
Multivariate versus univariate decision trees
– Brodley, Utgoff
- 1992
|
|
31
|
Linear machine decision trees
– Utgoff, Brodley
- 1991
|
|
29
|
Linear function neurons: structure and training
– Hampson, Volper
- 1986
|
|
26
|
An incremental method for finding multivariate splits for decision trees
– Utgoff
- 1990
|
|
25
|
Adaptive Decision Tree Algorithms for Learning From Examples
– Pagallo
- 1990
|
|
23
|
What should be minimized in a decision tree
– Fayyad, Irani
- 1990
|
|
17
|
Small nets and Short Paths: Optimising Neural Computation
– Frean
- 1990
|
|
14
|
Learning polynomial functions by feature construction
– Sutton, Matheus
- 1991
|
|
9
|
Pattern classification by iteratively determined linear and piecewise linear discriminant functions
– Duda, Fossum
- 1966
|
|
2
|
NADALINE: A normalized adaptive linear element that learns efficiently
– Sutton
- 1988
|
