程序包 weka.classifiers.rules
package weka.classifiers.rules
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类类说明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.Class for building and using a simple decision table majority classifier.
For more information see:
Ron Kohavi: The Power of Decision Tables.Class providing hash table keys for DecisionTableClass for building and using a decision table/naive bayes hybrid classifier.This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.Generates a decision list for regression problems using separate-and-conquer.Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.Class for generating a PART decision list.Class for building and using a PRISM rule set for classification.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.Abstract class of generic ruleThis 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.Class for building and using a 0-R classifier.