Abstract
Recent work in classification indicates that significant improvements in accuracy can be obtained by growing an ensemble of classifiers and having them vote for the most popular class. Implicit in many of these techniques is the concept of randomization that generates different classifiers. In this paper, they focus on ensembles of decision trees that are created using a randomized procedure based on histograms. Techniques, such as histograms, that discretize continuous variables, have long been used in classification to convert the data into a form suitable for processing and to reduce the compute time. The approach combines the ideas behind discretization through histograms and randomization in ensembles to create decision trees by randomly selecting a split point in an interval around the best bin boundary in the histogram. The experimental results with public domain data show that ensembles generated using this approach are competitive in accuracy and superior in computational cost to other ensembles techniques such as boosting and bagging.
Keywords
Boosting (machine learning)HistogramDecision treeComputer scienceDiscretizationArtificial intelligenceBinMachine learningDecision boundaryPattern recognition (psychology)Domain (mathematical analysis)Class (philosophy)Data miningMathematicsAlgorithmClassifier (UML)
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Publication Info
- Year
- 2002
- Type
- article
- Pages
- 370-383
- Citations
- 6
- Access
- Closed
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Cite This
Chandrika Kamath,
Erick Cantú‐Paz,
David Littau
(2002).
Approximate Splitting for Ensembles of Trees using Histograms.
, 370-383.
https://doi.org/10.1137/1.9781611972726.22
Identifiers
- DOI
- 10.1137/1.9781611972726.22