统计与决策2024，Vol.40Issue(22)：59-64,6.DOI:10.13546/j.cnki.tjyjc.2024.22.010
基于加速骨干二元粒子群优化的样本规约方法
Sample Specification Method Based on Accelerated Backbone Binary Particle Swarm Optimization
摘要
Abstract
Sample specification method is an outstanding data preprocessing paradigm in statistical machine learning,and it can be used to remove redundant samples and noise from labeled training sets,thus improving the performance of classification statistical algorithms.Although scholars have proposed a large number of sample specification methods based on evolutionary al-gorithms and proved their effectiveness,the existing sample specification methods based on evolutionary algorithms rely on too many parameters.Moreover,as the number of samples in labeled training sets increases,the existing sample specification meth-ods based on evolutionary algorithms have lower search efficiency and greater time overhead.In order to overcome these prob-lems,this paper proposes a sample specification method based on hybrid backbone binary particle swarm optimization(SRM-HB-PSO).In SRM-HBPSO,firstly,a hybrid backbone binary particle swarm optimization(HBPSO)algorithm combined with search space reduction strategy is designed.Then the labeled training set is optimized by HBPSO to obtain an optimized reduced subset.Finally,SRM-HBPSO trains a given classification statistical algorithm on the reduced subset that is optimized,thereby improving its performance.Simulation experiments show that,in terms of improving the average classification accuracy and improving the average sample reduction rate of the random forest classification statistical algorithm,SRM-HBPSO is superior to 5 advanced sample specification algorithms on 10 real benchmark data sets from the fields of finance,medical treatment and image.关键词
统计机器学习/分类统计算法/样本规约/随机森林/搜索空间约简策略Key words
statistical machine learning/classification statistical algorithm/sample specification/random forest/search space reduction strategy分类
数理科学引用本文复制引用
罗少甫,刘河..基于加速骨干二元粒子群优化的样本规约方法[J].统计与决策,2024,40(22):59-64,6.基金项目
重庆市教育委员会科学技术研究项目(KJQN202203007) （KJQN202203007）
重庆市教育科学规划项目(K22YG218233) （K22YG218233）
重庆市科研院所绩效激励引导专项项目(cstc2022jxj10214) （cstc2022jxj10214）
重庆市教委科学技术研究计划重点项目(KJZD-K202114401) （KJZD-K202114401）
