Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.

ABSTRACT: 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, an

SUBMITTER: Tian Y 

PROVIDER: S-EPMC4972358 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature