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VCPA-based hybrid strategy.zip
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文件大小: 795kb
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上传时间: 2020-07-11
详细说明：光谱信息的特征选择，通过云永欢等提出的VCPA来进行光谱信息的特征选择（文件中包含了VCPA，IRIV，VCPA-GA以及VCPA-IRIV等光谱的变量选择算法）。In this study, we propose a hybrid variable selection strategy based on the continuous shrinkage of variable space which is the core idea of variable combination population analysis (VCPA). The VCPA-based hybrid strategy continuously shrinks the variable space from big to small and optimizes it based on modified VCPA in the first step. It then employs iteratively retaining informative variables (IRIV) and a genetic algorithm (GA) to carry out further optimization in the second step. It takes full advantage of VCPA, GA, and IRIV, and makes up for their drawbacks in the face of high numbers of variables. Three NIR datasets and three variable selection methods including two widely-used methods (competitive adaptive reweighted sampling, CARS and genetic algorithm-interval partial least squares, GA–iPLS) and one hybrid method (variable importance in projection coupled with genetic algorithm, VIP–GA) were used to investigate the improvement of VCPA-based hybrid strategy.
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