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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… Expand
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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… Expand
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Highly Cited
2008
Highly Cited
2008
Learning from unbalanced datasets presents a convoluted problem in which traditional learning algorithms may perform poorly. The… Expand
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Highly Cited
2008
Highly Cited
2008
Semantic-based image retrieval has attracted great interest in recent years. This paper proposes a region-based image retrieval… Expand
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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… Expand
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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… Expand
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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… Expand
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Highly Cited
2004
Highly Cited
2004
The paper introduces meta decision trees (MDTs), a novel method for combining multiple classifiers. Instead of giving a… Expand
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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… Expand
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Highly Cited
1997
Highly Cited
1997
We explore a new approach to shape recognition based on a virtually infinite family of binary features (queries) of the image… Expand
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Highly Cited
1995
Highly Cited
1995
Syntactic natural language parsers have shown themselves to be inadequate for processing highly-ambiguous large-vocabulary text… Expand
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