Directly optimizing evaluation measures in learning to rank based on the clonal selection algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{He:2010:CIKM,
  author =       "Qiang He and Jun Ma and Shuaiqiang Wang",
  title =        "Directly optimizing evaluation measures in learning to
                 rank based on the clonal selection algorithm",
  booktitle =    "Proceedings of the 19th ACM international conference
                 on Information and knowledge management, CIKM '10",
  year =         "2010",
  pages =        "1449--1452",
  address =      "Toronto, ON, Canada",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, clonal
                 selection algorithm, information retrieval, learning to
                 rank, machine learning, ranking function: Poster",
  isbn13 =       "978-1-4503-0099-5",
  DOI =          "doi:10.1145/1871437.1871644",
  size =         "4 pages",
  acmid =        "1871644",
  abstract =     "One fundamental issue of learning to rank is the
                 choice of loss function to be optimised. Although the
                 evaluation measures used in Information Retrieval (IR)
                 are ideal ones, in many cases they can't be used
                 directly because they do not satisfy the smooth
                 property needed in conventional machine learning
                 algorithms. In this paper a new method named RankCSA is
                 proposed, which tries to use IR evaluation measure
                 directly. It employs the clonal selection algorithm to
                 learn an effective ranking function by combining
                 various evidences in IR. Experimental results on the
                 LETOR benchmark datasets demonstrate that RankCSA
                 outperforms the baseline methods in terms of P@n, MAP
                 and NDCG@n.",
}

Genetic Programming entries for Qiang He Jun Ma Shuaiqiang Wang

Citations