Package weka.classifiers.rules

Class Summary
ConjunctiveRule 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 Class for building and using a simple decision table majority classifier.

For more information see:

Ron Kohavi: The Power of Decision Tables.
DecisionTableHashKey Class providing hash table keys for DecisionTable
DTNB Class for building and using a decision table/naive bayes hybrid classifier.
FURIA FURIA: Fuzzy Unordered Rule Induction Algorithm

Details please see:

Jens Christian Huehn, Eyke Huellermeier (2009).
JRip This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.
M5Rules Generates a decision list for regression problems using separate-and-conquer.
NNge Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).
OLM This class is an implementation of the Ordinal Learning Method (OLM).
Further information regarding the algorithm and variants can be found in:

Arie Ben-David (1992).
OneR Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.
PART Class for generating a PART decision list.
Prism Class for building and using a PRISM rule set for classification.
Ridor 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.
Rule Abstract class of generic rule
RuleStats 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.
ZeroR Class for building and using a 0-R classifier.