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

Decision tree learning

Known as: Gini impurity, Regression tree, CART 
Decision tree learning uses a decision tree as a predictive model which maps observations about an item (represented in the branches) to conclusions… Expand
Wikipedia

Related topics

Related topics

50 relations
AdaBoostAffective computingAlternating decision treeBootstrap aggregating
Expand

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2010
Highly Cited
2010
Discrimination Aware Decision Tree Learning
Recently, the following discrimination aware classification problem was introduced: given a labeled dataset and an attribute B… Expand
  • figure 1
  • table 1
  • figure 2
  • figure 3
  • figure 4
Is this relevant?
Highly Cited
2008
Highly Cited
2008
Learning Decision Trees for Unbalanced Data
Learning from unbalanced datasets presents a convoluted problem in which traditional learning algorithms may perform poorly. The… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2008
Highly Cited
2008
Region-based image retrieval with high-level semantics using decision tree learning
Semantic-based image retrieval has attracted great interest in recent years. This paper proposes a region-based image retrieval… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2007
Highly Cited
2007
Data Mining with Decision Trees - Theory and Applications
Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the… Expand
Is this relevant?
Highly Cited
2006
Highly Cited
2006
A Fast Decision Tree Learning Algorithm
There is growing interest in scaling up the widely-used decision-tree learning algorithms to very large data sets. Although… Expand
  • figure 1
  • table 1
  • table 2
  • table 3
  • table 4
Is this relevant?
Highly Cited
2004
Highly Cited
2004
Failure diagnosis using decision trees
We present a decision tree learning approach to diagnosing failures in large Internet sites. We record runtime properties of each… Expand
Is this relevant?
Highly Cited
2004
Highly Cited
2004
Combining Classifiers with Meta Decision Trees
The paper introduces meta decision trees (MDTs), a novel method for combining multiple classifiers. Instead of giving a… Expand
  • figure 1
  • table 1
  • table 2
  • table 3
  • table 5
Is this relevant?
Highly Cited
1999
Highly Cited
1999
The Alternating Decision Tree Learning Algorithm
The application of boosting procedures to decision tree algorithms has been shown to produce very accurate classi ers. These… Expand
  • figure 1
  • figure 4
  • figure 5
  • figure 6
  • figure 7
Is this relevant?
Highly Cited
1997
Highly Cited
1997
Shape Quantization and Recognition with Randomized Trees
We explore a new approach to shape recognition based on a virtually infinite family of binary features (queries) of the image… Expand
Is this relevant?
Highly Cited
1995
Highly Cited
1995
Statistical Decision-Tree Models for Parsing
Syntactic natural language parsers have shown themselves to be inadequate for processing highly-ambiguous large-vocabulary text… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
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