title: 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, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
authors.forename: Yuling
authors.initials: Y
authors.lastname: Tian
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
authors.forename: Hongxian
authors.initials: H
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D000818:Animals; D001402:B-Lymphocytes; D035843:Biomedical Research; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D020407:Internet; D016239:MEDLINE; D000069550:Machine Learning; D039781:PubMed