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 a 1 ∧ a 2... ∧ a n ⇒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
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Communication dans un congrès
ISA: Information Security and Assurance, Aug 2011, Brno University, Czech Republic. Springer, 5th International Conference on Information Security and Assurance, pp.43-52, 2011, Communications in Computer and Information Science. 〈http://www.sersc.org/ISA2011〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00757482
Contributeur : Michel Liquiere <>
Soumis le : mardi 27 novembre 2012 - 10:05:26
Dernière modification le : jeudi 24 mai 2018 - 15:59:23

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  • HAL Id : lirmm-00757482, version 1

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Ines Bouzouita, Michel Liquière, Samir Elloumi, Ali Jaoua. A Comparative Study of a New Associative Classification Approach for Mining Rare and Frequent Classification Rules. ISA: Information Security and Assurance, Aug 2011, Brno University, Czech Republic. Springer, 5th International Conference on Information Security and Assurance, pp.43-52, 2011, Communications in Computer and Information Science. 〈http://www.sersc.org/ISA2011〉. 〈lirmm-00757482〉

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