Scope Classification: An Instance-Based Learning Algorithm with a Rule-Based Characterisation
نویسندگان
چکیده
Scope classiication is a new instance-based learning (IBL) technique with a rule-based characterisation. Within the scope approach, the classiication of an object o is based on the examples that are closer to o than every example labelled with another class. In contrast to standard distance-based IBL classiiers, scope classiication relies on partial pre-orderings o between examples, indexed by objects. Interestingly, the notion of closeness to o that is used characterises the classes predicted by all the rules that cover o and are relevant and consistent for the training set. Accordingly, scope classiication is an IBL technique with a rule-based characterisation. Since rules do not have to be explicitly generated, the scope approach applies to classiication problems where the number of rules prevents them from being exhaustively computed.
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