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在这项研究中,提出了一种基于模型群体分析(MPA)思想的变量选择优化算法——可变迭代空间收缩法(VISSA)。与大多数现有的变量选择优化方法不同,VISSA 在优化的每个步骤中都对变量空间的性能进行统计评估。提出加权二元矩阵采样(WBMS)来生成跨越变量子空间的子模型。在优化过程中突出显示了两个规则。首先,变量空间在每一步中都会缩小。其次,新的变量空间优于前一个变量空间。第二条规则在大多数现有方法中很少得到满足,它是 VISSA 策略的核心。与竞争自适应重加权抽样(CARS)、蒙特卡罗无信息变量消除(MCUVE)和迭代保留信息变量(IRIV)等一些有前途的变量选择方法相比,VISSA对NIR数据的标定表现出更好的预测能力。
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VISSA.zip (9个子文件)
VISSA
Example.m 694B
predict.m 560B
wheat_kernel.mat 192KB
pls.m 2KB
pretreat.m 786B
plscvfold.m 3KB
plsnipals.m 2KB
vissa.m 5KB
license.txt 1KB
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