• Facets (new session)
• Description
• Metadata
• Settings
  • owl:sameAs
  • Inference Rule: b3sb3sifpdbprdf-labelfacetshttp://dbpedia.org/resource/inference/rules/dbpedia#http://dbpedia.org/resource/inference/rules/opencyc#http://dbpedia.org/resource/inference/rules/umbel#http://dbpedia.org/resource/inference/rules/yago#http://dbpedia.org/schema/property_rules#http://www.ontologyportal.org/inference/rules/SUMO#http://www.ontologyportal.org/inference/rules/WordNet#http://www.w3.org/2002/07/owl#ldpoplwebskos-transvirtrdf-labelNone
    

DecisionRule: a subclass of Decision strategy that selects a course of action by applying a yes/no rule. The various instances of decision rules are based on the specific test or criterion used. In data mining, a decision rule is most often applied to a sequence of scalar values that represent measures of quality (e.g. estimated feature quality), performance (e.g. misclassification rate), probability, etc. The test takes the form of a triple , where Focus represents the specific criterion on which the decision is based: the observed values themselves, their ranks or their probabilities. RelOp is one of the relational operators {LessThan, Leq, Eq, GreaterThan, Geq}, and Threshold is the cutoff point on the magnitude, rank, percentage or probability. Note that Threshold is not necessarily a constant, it can be a function (e.g., max, min) of the observed values. For example, the Max Rule is a particular case of the Top K Rule where K=1, and the Maximum a posteriori rule is a special case of the Max rule, where the focus is a probability distribution the RelOp is Eq, and the threshold is the maximum of the observed probabilities.