AGGE: A Novel Method to Automatically Generate Rule Induction Classifiers Using Grammatical Evolution

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  author =       "Romaissaa Mazouni and Abdellatif Rahmoun",
  title =        "{AGGE}: A Novel Method to Automatically Generate Rule
                 Induction Classifiers Using Grammatical Evolution",
  booktitle =    "IDC 2014",
  year =         "2014",
  editor =       "David Camacho and Lars Braubach and 
                 Salvatore Venticinque and Costin Badica",
  volume =       "570",
  series =       "Studies in Computational Intelligence",
  pages =        "279--288",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, agge: automatic generation of classifiers
                 using grammatical evolution, context free grammar, rule
                 induction algorithms, data mining, rule based
  isbn13 =       "978-3-319-10421-8",
  bibdate =      "2014-10-09",
  bibsource =    "DBLP,
  DOI =          "doi:10.1007/978-3-319-10422-5_30",
  abstract =     "One of the main and fundamental tasks of data mining
                 is the automatic induction of classification rules from
                 a set of examples and observations. A variety of
                 methods performing this task have been proposed in the
                 recent literature. Many comparative studies have been
                 carried out in this field. However, the main common
                 feature between these methods is that they are designed
                 manually. In the meanwhile, there have been some
                 successful attempts to automatically design such
                 methods using Grammar-based Genetic Programming (GGP).
                 In this paper, we propose a different system called
                 Automatic Grammar Genetic Programming (AGGP) that can
                 evolve complete java program codes. These codes
                 represent a rule induction algorithm that uses a
                 grammar evolution technique that governs a Backus Naur
                 Form grammar definition mapping to a program. To
                 perform this task, we will use binary strings as inputs
                 to the mapper along with the Backus Naur Form grammar.
                 Such binary strings represent possible potential
                 solutions resulting from the initialised component and
                 Weka building blocks, this would ease the induction
                 process and makes induced programs short. Experimental
                 results prove the efficiency of the proposed method. It
                 is also shown that, compared to some recent and similar
                 manual techniques (Prism, Ripper, Ridor, OneRule) the
                 proposed method outperforms such techniques.A benchmark
                 of well-known data sets is used for the sake of

Genetic Programming entries for Romaissaa Mazouni Abdellatif Rahmoun