An immune programming-based ranking function discovery approach for effective information retrieval

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@Article{Wang:2010:esa,
  author =       "Shuaiqiang Wang and Jun Ma and Qiang He",
  title =        "An immune programming-based ranking function discovery
                 approach for effective information retrieval",
  journal =      "Expert Systems with Applications",
  year =         "2010",
  volume =       "37",
  number =       "8",
  pages =        "5863--5871",
  keywords =     "genetic algorithms, genetic programming, Information
                 retrievalInformation retrieval, Learning to rank,
                 Immune programming, Evolutionary computation, Machine
                 learning",
  ISSN =         "0957-4174",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-4YDT3VY-1/2/5d51ce9997fad3e696db420a8662f16f",
  DOI =          "doi:10.1016/j.eswa.2010.02.019",
  abstract =     "In this paper, we propose RankIP, the first immune
                 programming (IP) based ranking function discovery
                 approach. IP is a novel evolution based machine
                 learning algorithm with the principles of immune
                 systems, which is verified to be superior to Genetic
                 Programming (GP) on the convergence of algorithm
                 according to their experimental results in Musilek et
                 al. (2006). However, such superiority of IP is mainly
                 demonstrated for optimization problems. RankIP adapts
                 IP to the learning to rank problem, a typical
                 classification problem. In doing this, the solution
                 representation, affinity function, and high-affinity
                 antibody selection require completely different
                 treatments. Besides, two formulae focusing on selecting
                 best antibody for test are designed for learning to
                 rank.

                 Experimental results demonstrate that the proposed
                 RankIP outperforms the state-of-the-art learning-based
                 ranking methods significantly in terms of P@n,MAP and
                 NDCG@n.",
  notes =        "Also known as \cite{Wang20105863}",
}

Genetic Programming entries for Shuaiqiang Wang Jun Ma Qiang He

Citations