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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
2020
Highly Cited
2020
NBDT: Neural-Backed Decision Trees
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep… 
Highly Cited
2013
Highly Cited
2013
Robust Online Dynamic Security Assessment Using Adaptive Ensemble Decision-Tree Learning
Online dynamic security assessment (DSA) is examined in a data-mining framework by taking into account the operating condition… 
Review
2013
Review
2013
A survey of cost-sensitive decision tree induction algorithms
The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of… 
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… 
Highly Cited
2009
Highly Cited
2009
Temporal and Spatial Dynamics of Diel-Cycling Hypoxia in Estuarine Tributaries
The distribution and intensity of hypoxia (low dissolved oxygen) in estuaries is increasing worldwide due to cultural… 
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… 
Highly Cited
2007
Highly Cited
2007
K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods
In this paper, we present "k-means+ID3", a method to cascade k-means clustering and the ID3 decision tree learning methods for… 
Highly Cited
2007
Highly Cited
2007
Cost-Sensitive Decision Trees Applied to Medical Data
Classification plays an important role in medicine, especially for medical diagnosis. Health applications often require… 
Highly Cited
2006
Highly Cited
2006
TREE MORTALITY DURING EARLY FOREST DEVELOPMENT: A LONG-TERM STUDY OF RATES, CAUSES, AND CONSEQUENCES
Tree mortality is a critical but understudied process in coniferous forest development. Current successional models assume that… 
Highly Cited
1995
Highly Cited
1995
Robust Decision Trees: Removing Outliers from Databases
Finding and removing outliers is an important problem in data mining. Errors in large databases can be extremely common, so an… 
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