Skip to search formSkip to main content
You are currently offline. Some features of the site may not work correctly.

Decision tree

Known as: Probability tree, Decision graph, Decision-tree 
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event… Expand
Wikipedia

Related topics

Related topics

50 relations
Amiga productivity softwareAssociation rule learningBehavior modelBias–variance tradeoff
Expand

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2017
Highly Cited
2017
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithm, and has quite a few effective implementations… Expand
  • table 1
  • figure 2
  • table 2
  • table 3
Highly Cited
2004
Highly Cited
2004
Induction of Decision Trees
The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in… Expand
  • figure 1
  • table 1
  • figure 2
  • figure 3
  • figure 4
Highly Cited
2004
Highly Cited
2004
Incremental Induction of Decision Trees
  • P. Utgoff
  • Machine Learning
  • 2004
    • Corpus ID: 5293072
This article presents an incremental algorithm for inducing decision trees equivalent to those formed by Quinlan's nonincremental… Expand
  • table 5
  • table 6
  • table 7
  • table 8
  • table 9
Highly Cited
2004
Highly Cited
2004
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a… Expand
  • table 1
  • table 2
  • figure 1
  • figure 2
  • figure 3
Highly Cited
1998
Highly Cited
1998
Quantum computation and decision trees
Many interesting computational problems can be reformulated in terms of decision trees. A natural classical algorithm is to then… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Highly Cited
1996
Highly Cited
1996
Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid
Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classification tasks even when the… Expand
  • figure 2
  • figure 2
  • table 1
  • figure 4
  • figure 5
Highly Cited
1994
Highly Cited
1994
Probabilistic part-of-speech tagging using decision trees
In this paper, a new probabilistic tagging method is presented which avoids problems that Markov Model based taggers face, when… Expand
Highly Cited
1994
Highly Cited
1994
A System for Induction of Oblique Decision Trees
This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill… Expand
  • figure 5
  • table 1
  • figure 9
  • table 2
Review
1991
Review
1991
A survey of decision tree classifier methodology
A survey is presented of current methods for decision tree classifier (DTC) designs and the various existing issues. After… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Highly Cited
1976
Highly Cited
1976
Constructing Optimal Binary Decision Trees is NP-Complete
We demonstrate that cons&g optimal binary de&ion trees ia an NP=compkt.e probtem, where an op timal tree is one which minin&s the… Expand
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy, Terms of Service, and Dataset License
ACCEPT & CONTINUE