Data Mining Tools See5 and C5.0

By J. Ross Quinlan. 2004

336 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

An optimization approach to learning falling rule lists (2017, methodology): “0 (Quinlan, 2004), and decision list learning (Rivest, 1987).”
User re-authentication via mouse movements (2004, methodology): “For details regarding the specics of C5.0 the reader is referred to [29, 30 ].”
Using Predicate-Argument Structures for Information Extraction (2003, methodology): “Decision tree learning provided by C5 (Quinlan, 2002) has built in support for boosting.”
A review of epileptic seizure detection using machine learning classifiers (2020, background): “0 decision tree [121] algorithm to explore the logic rules for seizure detection, with an average accuracy of 75%.”
Understanding Short-term Changes in Online Activity Sessions (2017, background): “0 classiﬁer, which is a decision tree based classi-ﬁer with feature selection [18].”
Support Vector Learning for Semantic Argument Classification (2005, methodology): “They then report performance gains by adding additional features (Surdeanu System II) All the feature are combined with a decision tree classifier—C5 (Quinlan, 1986, 2003).”
A review of associative classification mining (2007, methodology): “The latter, also called backward pruning, is more popular and has been used by many decision tree algorithms such C4.5 and See5 (Quinlan, 1998).”
Review and Comparison of Associative Classification Data Mining Approaches (2014, unknown): “Decision tree algorithms like C4.5 [10] and See5 [42] are used pessimistic error estimation.”