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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… 
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Papers overview

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Highly Cited
2010
Highly Cited
2010
Recently, the following discrimination aware classification problem was introduced: given a labeled dataset and an attribute B… 
Highly Cited
2009
Highly Cited
2009
Stochastic Gradient Boosted Decision Trees (GBDT) is one of the most widely used learning algorithms in machine learning today… 
Highly Cited
2007
Highly Cited
2007
Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the… 
Highly Cited
2006
Highly Cited
2006
There is growing interest in scaling up the widely-used decision-tree learning algorithms to very large data sets. Although… 
Review
2005
Review
2005
Decision trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various… 
Review
2004
Review
2004
Decision trees have proved to be valuable tools for the description, classification and generalization of data. Work on… 
Highly Cited
2004
Highly Cited
2004
We present a decision tree learning approach to diagnosing failures in large Internet sites. We record runtime properties of each… 
Highly Cited
1999
Highly Cited
1999
The application of boosting procedures to decision tree algorithms has been shown to produce very accurate classi ers. These… 
Highly Cited
1995
Highly Cited
1995
Syntactic natural language parsers have shown themselves to be inadequate for processing highly-ambiguous large-vocabulary text… 
Highly Cited
1995
Highly Cited
1995
Finding and removing outliers is an important problem in data mining. Errors in large databases can be extremely common, so an…