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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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Related topics

Related topics

50 relations
AdaBoostAffective computingAlternating decision treeBootstrap aggregating

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2010
Highly Cited
2010
Filter, Rank, and Transfer the Knowledge: Learning to Chat
Learning to chat is a fascinating machine learning task with many applications from user-modeling to artificial intelligence… 
Review
2008
Review
2008
Predicting habitat suitability for the endemic mountain nyala (Tragelaphus buxtoni) in Ethiopia
The use of statistical models to predict species distributions and suitable habitat has become an essential tool for wildlife… 
2008
2008
Physical Factors Control Phytoplankton Production and Nitrogen Fixation in Eight Texas Reservoirs
We compared regression tree analyses and multiple linear regression models to explore the relative importance of physical factors… 
Review
2006
Review
2006
Cultivated Helianthus annuus (Asteraceae) volunteers as a genetic “bridge” to weedy sunflower populations in North America
In many crops, unharvested seeds can create populations of volunteer plants that increase opportunities for crop-to-wild gene… 
Highly Cited
2005
Highly Cited
2005
A Comparative Study of Medical Data Classification Methods Based on Decision Tree and System Reconstruction Analysis
This paper studies medical data classification methods, comparing decision tree and system reconstruction analysis as applied to… 
Highly Cited
2005
Highly Cited
2005
Wavelet-based texture classification of tissues in computed tomography
The research presented in this article is aimed at developing an automated imaging system for classification of tissues in… 
2003
2003
Reinforcement Learning with Decision Trees
We present a decision tree based approach to function approximation in reinforcement learning. We compare our approach with table… 
2003
2003
On the quest for easy-to-understand splitting rules
1999
1999
Snowpack factors associated with strength changes of buried surface hoar layers
1996
1996
Compilation of Weighted Finite-State Transducers from Decision Trees
We report on a method for compiling decision trees into weighted finite-state transducers. The key assumptions are that the tree… 
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