Given a class model built from a dataset including labeled data, classification assigns a new data object to the appropriate class. In associative classification the class model (i.e., the classifier) is a set of association rules. Associative classification is a promising technique for the generation of highly accurate classifiers. In this article, we present a compact form which encodes without information loss the classification knowledge available in a classification rule set. This form includes the rules that are essential for classification purposes, and thus it can replace the complete rule set. The proposed form is particularly effective in dense datasets, where traditional extraction techniques may generate huge rule sets. The reduction in size of the rule set allows decreasing the complexity of both the rule generation step and the rule pruning step. Hence, classification rule extraction can be performed also with low support, in order to extract more, possibly useful, rules.

Essential classification rule sets / Baralis, ELENA MARIA; Chiusano, SILVIA ANNA. - In: ACM TRANSACTIONS ON DATABASE SYSTEMS. - ISSN 0362-5915. - STAMPA. - 29 (4):(2004), pp. 635-674. [10.1145/1042046.1042048]

Essential classification rule sets

BARALIS, ELENA MARIA;CHIUSANO, SILVIA ANNA
2004

Abstract

Given a class model built from a dataset including labeled data, classification assigns a new data object to the appropriate class. In associative classification the class model (i.e., the classifier) is a set of association rules. Associative classification is a promising technique for the generation of highly accurate classifiers. In this article, we present a compact form which encodes without information loss the classification knowledge available in a classification rule set. This form includes the rules that are essential for classification purposes, and thus it can replace the complete rule set. The proposed form is particularly effective in dense datasets, where traditional extraction techniques may generate huge rule sets. The reduction in size of the rule set allows decreasing the complexity of both the rule generation step and the rule pruning step. Hence, classification rule extraction can be performed also with low support, in order to extract more, possibly useful, rules.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/1397552
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