Generating term weighting schemes through genetic programming

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@InProceedings{Mazyad:2018:GECCOcomp,
  author =       "Ahmad Mazyad and Fabien Teytaud and Cyril Fonlupt",
  title =        "Generating term weighting schemes through genetic
                 programming",
  booktitle =    "GECCO '18: Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  year =         "2018",
  editor =       "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and 
                 Shigeru Obayashi and Bogdan Filipic and 
                 Thomas Bartz-Beielstein and Grant Dick and 
                 Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and 
                 Pablo Valledor Pellicer and Manuel Lopez-Ibanez and 
                 Daniel R. Tauritz and Pietro S. Oliveto and 
                 Thomas Weise and Borys Wrobel and Ales Zamuda and 
                 Anne Auger and Julien Bect and Dimo Brockhoff and 
                 Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and 
                 Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and 
                 Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and 
                 Richard Duro and Joshua Auerbach and 
                 Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and 
                 Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and 
                 Francisco {Chavez de la O} and Ozgur Akman and 
                 Khulood Alyahya and Juergen Branke and Kevin Doherty and 
                 Jonathan Fieldsend and Giuseppe Carlo Marano and 
                 Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and 
                 Stefan Wagner and Michael Affenzeller and 
                 Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and 
                 Riyad Alshammari and Tokunbo Makanju and 
                 Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and 
                 John R. Woodward and Shin Yoo and John McCall and 
                 Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and 
                 Masaya Nakata and Anthony Stein and 
                 Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and 
                 Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton and William {La Cava} and 
                 Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and 
                 Ivanoe {De Falco} and Antonio {Della Cioppa} and 
                 Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and 
                 Giovanni Iacca and Ahmed Hallawa and Anil Yaman and 
                 Alma Rahat and Handing Wang and Yaochu Jin and 
                 David Walker and Richard Everson and Akira Oyama and 
                 Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and 
                 Pramudita Satria Palar",
  isbn13 =       "978-1-4503-5764-7",
  pages =        "268--269",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205651.3205799",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "erm-Weighting Scheme (TWS) is an important step in
                 text classification. It determines how documents are
                 represented in Vector Space Model (VSM). Even though
                 state-of-the-art TWSs exhibit good behaviours, a large
                 number of new works propose new approaches and new TWSs
                 that improve performances. Furthermore, it is still
                 difficult to tell which TWS is well suited for a
                 specific problem. In this paper, we are interested in
                 automatically generating new TWSs with the help of
                 evolutionary algorithms and especially genetic
                 programming (GP). GP evolves and combines different
                 statistical information and generates a new TWS based
                 on the performance of the learning method. We
                 experience the generated TWSs on three well-known
                 benchmarks. Our study shows that even early generated
                 formulas are quite competitive with the
                 state-of-the-art TWSs and even in some cases outperform
                 them.",
  notes =        "Also known as \cite{3205799} GECCO-2018 A
                 Recombination of the 27th International Conference on
                 Genetic Algorithms (ICGA-2018) and the 23rd Annual
                 Genetic Programming Conference (GP-2018)",
}

Genetic Programming entries for Ahmad Mazyad Fabien Teytaud Cyril Fonlupt

Citations