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ridor from group nz.ac.waikato.cms.weka (version 1.0.2)
An implementation of a RIpple-DOwn Rule learner.
It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate. Then it generates the "best" exceptions for each exception and iterates until pure. Thus it performs a tree-like expansion of exceptions.The exceptions are a set of rules that predict classes other than the default. IREP is used to generate the exceptions.
For more information about Ripple-Down Rules, see:
Brian R. Gaines, Paul Compton (1995). Induction of Ripple-Down Rules Applied to Modeling Large Databases. J. Intell. Inf. Syst. 5(3):211-228.
Artifact ridor
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Tags: brian least exception used until more tree syst iterates information compton generates induction intell like that pure paul default than then ripple each large expansion other generate about 1995 exceptions rate weighted rule best performs classes gaines with error thus learner irep rules implementation predict modeling databases applied down first
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/ridor
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Tags: brian least exception used until more tree syst iterates information compton generates induction intell like that pure paul default than then ripple each large expansion other generate about 1995 exceptions rate weighted rule best performs classes gaines with error thus learner irep rules implementation predict modeling databases applied down first
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/ridor
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
ridor from group nz.ac.waikato.cms.weka (version 1.0.1)
An implementation of a RIpple-DOwn Rule learner.
It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate. Then it generates the "best" exceptions for each exception and iterates until pure. Thus it performs a tree-like expansion of exceptions.The exceptions are a set of rules that predict classes other than the default. IREP is used to generate the exceptions.
For more information about Ripple-Down Rules, see:
Brian R. Gaines, Paul Compton (1995). Induction of Ripple-Down Rules Applied to Modeling Large Databases. J. Intell. Inf. Syst. 5(3):211-228.
Artifact ridor
Group nz.ac.waikato.cms.weka
Version 1.0.1
Last update 24. April 2012
Tags: brian least exception used until more tree syst iterates information compton generates induction intell like that pure paul default than then ripple each large expansion other generate about 1995 exceptions rate weighted rule best performs classes gaines with error thus learner irep rules implementation predict modeling databases applied down first
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/ridor
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.1
Last update 24. April 2012
Tags: brian least exception used until more tree syst iterates information compton generates induction intell like that pure paul default than then ripple each large expansion other generate about 1995 exceptions rate weighted rule best performs classes gaines with error thus learner irep rules implementation predict modeling databases applied down first
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/ridor
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, weka-dev,
There are maybe transitive dependencies!
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