GPQ: Directly Optimizing Q-measure based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/cikm/LinLZX14,
  author =       "Yuan Lin and Hongfei Lin and Ping Zhang and Bo Xu",
  title =        "{GPQ}: Directly Optimizing {Q}-measure based on
                 Genetic Programming",
  booktitle =    "Proceedings of the 23rd ACM International Conference
                 on Conference on Information and Knowledge Management,
                 CIKM 2014",
  publisher =    "ACM",
  year =         "2014",
  editor =       "Jianzhong Li and Xiaoyang Sean Wang and 
                 Minos N. Garofalakis and Ian Soboroff and Torsten Suel and 
                 Min Wang",
  address =      "Shanghai, China",
  month =        nov # " 3-7",
  pages =        "1859--1862",
  keywords =     "genetic algorithms, genetic programming, information
                 retrieval, learning to rank, q-measure",
  isbn13 =       "978-1-4503-2598-1",
  bibdate =      "2014-11-07",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/cikm/cikm2014.html#LinLZX14",
  URL =          "http://dl.acm.org/citation.cfm?id=2661829",
  DOI =          "doi:10.1145/2661829.2661932",
  acmid =        "2661932",
  abstract =     "Ranking plays an important role in information
                 retrieval system. In recent years, a kind of research
                 named learning to rank becomes more and more popular,
                 which applies machine learning technology to solve
                 ranking problems. Lots of ranking models belonged to
                 learning to rank have been proposed, such as
                 Regression, RankNet, and ListNet. Inspired by this, we
                 proposed a novel learning to rank algorithm named GPQ
                 in this paper, in which genetic programming was
                 employed to directly optimize Q-measure evaluation
                 metric. Experimental results on OHSUMED benchmark
                 dataset indicated that our method GPQ could be
                 competitive with Ranking SVM, SVMMAP and ListNet, and
                 improve the ranking accuracies.",
}

Genetic Programming entries for Yuan Lin Hongfei Lin Ping Zhang Bo Xu

Citations