Research on B Cell Algorithm for Learning to Rank Method Based on Parallel StrategyReport as inadecuate




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For the purposes of information retrieval, users must find highly relevant documents from within a system and often a quite large one comprised of many individual documents based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic–there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.



Author: Yuling Tian , Hongxian Zhang

Source: http://plos.srce.hr/



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