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