Learn-One-Rule Algorithm

Learn-One-Rule(target_attribute, attributes, examples, k)
;Returns a single rule that covers some of the Examples
  best-hypothesis = the most general hypothesis
  candidate-hypotheses = {best-hypothesis}
  while candidate-hypothesis     ;Generate the next more specific candidate-hypotheses
    all-constraints = all "att.=val." contraints
    new-candidate-hypotheses = all specializations of candidate-hypotheses by adding all-constraints
    remove from new-candidate-hypotheses any that are duplicates, inconsistent, or not maximally specific
    ;Update best-hypothesis
    best-hypothesis = argmax h new-candidate-hypotheses Performance(h,examples,target_attribute)
    ;Update candidate-hypotheses
    candidate-hypotheses = the k best from new-candidate-hypotheses according to Performance.
  prediction = most frequent value of target_attribute from examples that match best-hypothesis
  return IF best-hypothesis THEN prediction

Performance(h, examples, target_attribute)
  h-examples = the set of examples that match h
  return - Entropy(h-examples) wrt target_attribute