基于随机森林的图书馆集成信息检索方法

Library Integrated Information Retrieval Method Based on Random Forest

为了全面提升图书馆集成信息检索方法的综合性能,结合随机森林算法,提出一种基于随机森林的图书馆集成信息检索方法。在建立随机森林底层分类器的过程中加入贝叶斯多项式,利用算法中携带的数据提出一种基于二维权重分布的投票机制,根据投票结果进行图书馆集成信息检索。仿真实验结果表明,所提方法能够有效提升检索效率和检索结果准确率,获取满意的图书馆集成信息检索结果。

In order to comprehensively improve the comprehensive performance of the library integrated information retrieval method,combined with the random forest algorithm,a library integrated information retrieval method based on random forest is proposed.Bayesian polynomials are added in the process of building a random forest bottom classifier.Using the data carried in the algorithm,a voting mechanism based on two-dimensional weight distribution is proposed,and the library integrated information retrieval is carried out according to the voting results.The simulation experiment results show that the proposed method can effectively improve the retrieval efficiency and the accuracy of retrieval results,and obtain satisfactory library integrated information retrieval results.