一种基于改进随机森林的知识检索优化算法

A Knowledge Retrieval Optimization Algorithm Based on Improved Random Forest

针对知识库知识检索优化过程中的数据冗余及效率较低问题,提出一种基于改进随机森林的知识检索优化算法,以提高知识检索优化效率。该算法采用互信息加权,并结合粒子群算法依据决策树的相关性及评价精度确定决策树权重,以优化随机森林的结构,最终获得耦合度更小且评价精度更高的算法模型。实验表明,该算法知识检索质量的NDCG指标平均提升了14.25%,平均评价精度指标MAP则提升了13.75%,表明该算法能提升检索质量,可用于解决检索结果优化效率和精度低的问题。

Aiming at the problem of data redundancy and low efficiency in knowledge retrieval optimization of knowledge base,a knowledge retrieval optimization algorithm based on improved random forest was proposed to improve the optimization efficiency of knowledge retrieval.This algorithm adopts mutual information weighting method and combines with particle swarm optimization algorithm to determine the weight of decision tree according to the relevance of decision tree and the evaluation accuracy,so as to optimize the structure of forest,and finally obtain the algorithm model with lower coupling degree and higher evaluation.Experiments show that the NDCG index of knowledge retrieval quality of the algorithm is improved by 14.25% on average,and the mean index of average evaluation accuracy(MAP)is improved by 13.75%,indicating that the algorithm can improve the retrieval quality and solve the problem of low optimization efficiency and accuracy of retrieval results.