Aggregating Interestingness Measures in Associative Classifiers

• Matheus Freitas da Silva UNESP
• Veronica Oliveira de Carvalho UNESP

DOI: https://doi.org/10.5753/eniac.2018.4405

Abstract

Associative classification, which has been widely used in several domains, aims to obtain a predictive model in which the process is based on the extraction of association rules. Model generation occurs in steps, one of them aimed at ordering and pruning a set of rules. Regarding ordering, one of the solutions is to rank the rules by means of objective measures (OMs). The ordering criterion impacts the accuracy of the classifier. In the literature’s works the measures are explored individually. Based on the exposed, this work aims to explore the aggregation of measures, in which several OMs are considered at the same time, in the context of associative classifiers.

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