A comparative study of a new associative classification approach for mining rare and frequent classification rules

Abstract

In this paper, we tackled the problem of generation of rare classification rules. Our work is motivated by the search of an effective algorithm allowing the extraction of rare classification rules by avoiding the generation of a large number of patterns at reduced time. Within this framework we are interested in rules of the form a1 ∧ a2... ∧ an ⇒ b which allow us to propose a new approach based on genetic algorithms principle. This approach allows obtaining frequent and rare rules while avoiding making a breadth search. We describe our method and provide a comparative study of three versions of our method on standard benchmark data sets. © 2011 Springer-Verlag.