| weka.classifiers.rules |
|
| Java Source File Name | Type | Comment |
| ConjunctiveRule.java | Class |
This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.
A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. |
| DecisionTable.java | Class |
Class for building and using a simple decision table majority classifier.
For more information see:
Ron Kohavi: The Power of Decision Tables. |
| DecisionTableHashKey.java | Class | |
| JRip.java | Class |
This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W. |
| M5Rules.java | Class |
Generates a decision list for regression problems using separate-and-conquer. |
| NNge.java | Class |
Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules). |
| OneR.java | Class |
Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes. |
| PART.java | Class |
Class for generating a PART decision list. |
| Prism.java | Class |
Class for building and using a PRISM rule set for classification. |
| Ridor.java | Class |
The 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. |
| Rule.java | Class | |
| RuleStats.java | Class | This class implements the statistics functions used in the
propositional rule learner, from the simpler ones like count of
true/false positive/negatives, filter data based on the ruleset, etc.
to the more sophisticated ones such as MDL calculation and rule
variants generation for each rule in the ruleset. |
| ZeroR.java | Class |
Class for building and using a 0-R classifier. |