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C4.5 algorithm

Known as: C5.0 algorithm, See5 algorithm, C5.0 
C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. C4.5 is an extension of Quinlan's earlier ID3 algorithm. The… 
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Related topics

Related topics

15 relations
Data miningDecision tree learningDocument classificationEntropy (information theory)

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2014
Highly Cited
2014
An Efficient Hybrid Intrusion Detection System based on C5.0 and SVM
Nowadays, much attention has been paid to intrusion detection system (IDS) which is closely linked to the safe use of network… 
Highly Cited
2011
Highly Cited
2011
A Hybrid Higher Order Neural Classifier for handling classification problems
Highly Cited
2011
Highly Cited
2011
Classifying eye and head movement artifacts in EEG signals
Brain Computer Interfaces has some exciting prospects such as controlling devices at the speed of thought. However BCI technology… 
Highly Cited
2009
Highly Cited
2009
MReC4.5: C4.5 Ensemble Classification with MapReduce
Classification is a significant technique in data mining research and applications. C4.5 is a widely used classification method… 
2009
2009
A Dynamic Discretization Approach for Constructing Decision Trees with a Continuous Label
In traditional decision (classification) tree algorithms, the label is assumed to be a categorical (class) variable. When the… 
Highly Cited
2008
Highly Cited
2008
Pattern Trees Induction: A New Machine Learning Method
Fuzzy classification is one of the most important applications in fuzzy set and fuzzy-logic-related research. Its goal is to find… 
Highly Cited
2008
Highly Cited
2008
CSMC: A combination strategy for multi-class classification based on multiple association rules
Highly Cited
2007
Highly Cited
2007
Polish tagger TaKIPI: rule based construction and optimization
A large number of different tags, limited corpora and the free word order are the main causes of low accuracy of tagging in… 
2007
2007
Combining multiple class distribution modified subsamples in a single tree
Highly Cited
2006
Highly Cited
2006
Improving rule sorting, predictive accuracy and training time in associative classification
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