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A decision list (also called an ordered rule set) is a collection of individual classification rules that collectively form a classifier. In contrast to an unordered rule set, decision lists have an inherent order, which makes classification quite straightforward. For classifying a new instance, the rules are tried in order, and the class of the first rule that covers the instance is predicted. If no induced rule fires, a default rule is invoked, which typically predicts the majority class.
Typically, decision lists are learned with a covering algorithm, which learns one rule at a time, appends it to the list, and removes all covered examples before learning the next one. Decision lists are popular in inductive logic programming, because PROLOG programs may be considered to be simple decision lists, where all rules predict the same concept.
A formal definition of decision lists, a comparison of their expressiveness to decision trees and rule sets in...
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Rivest RL (1987) Learning decision lists. Mach Learn 2:229–246
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Fürnkranz, J. (2017). Decision List. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_64
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_64
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