GPQ: Directly Optimizing Q-measure based on Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{conf/cikm/LinLZX14,
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author = "Yuan Lin and Hongfei Lin and Ping Zhang and Bo Xu",
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title = "{GPQ}: Directly Optimizing {Q}-measure based on
Genetic Programming",
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booktitle = "Proceedings of the 23rd ACM International Conference
on Conference on Information and Knowledge Management,
CIKM 2014",
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publisher = "ACM",
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year = "2014",
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editor = "Jianzhong Li and Xiaoyang Sean Wang and
Minos N. Garofalakis and Ian Soboroff and Torsten Suel and
Min Wang",
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address = "Shanghai, China",
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month = nov # " 3-7",
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pages = "1859--1862",
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keywords = "genetic algorithms, genetic programming, information
retrieval, learning to rank, q-measure",
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isbn13 = "978-1-4503-2598-1",
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bibdate = "2014-11-07",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/cikm/cikm2014.html#LinLZX14",
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URL = "http://dl.acm.org/citation.cfm?id=2661829",
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DOI = "doi:10.1145/2661829.2661932",
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acmid = "2661932",
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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