Open AccessResearch on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy
Author(s) -
Yanan Tian,
Hongxian Zhang
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0157994
Subject(s) - learning to rank , ranking (information retrieval) , computer science , ranking svm , rank (graph theory) , relevance (law) , machine learning , algorithm , convergence (economics) , artificial intelligence , selection (genetic algorithm) , clonal selection algorithm , information retrieval , data mining , artificial immune system , mathematics , combinatorics , political science , law , economics , economic growth
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.