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首页> 外文期刊>The Analyst: The Analytical Journal of the Royal Society of Chemistry: A Monthly International Publication Dealing with All Branches of Analytical Chemistry >A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling
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A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling

机译：一种新颖的变量选择方法，使用加权二进制矩阵采样迭代地优化变量空间

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摘要

In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: https://sourceforge.net/projects/multivariateanalysis/ files/VISSA/.
机译：在这项研究中，基于模型总体分析（MPA）的思想，提出了一种新的名为变量迭代空间收缩方法（VISSA）的优化算法用于变量选择。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统计地评估变量空间的性能。提出了加权二进制矩阵采样（WBMS）来生成跨越可变子空间的子模型。优化过程中将突出显示两个规则。首先，可变空间在每个步骤中都会缩小。第二，新的可变空间优于上一个。第二条规则是VISSA策略的核心，在大多数现有方法中很少满足。与竞争性自适应加权抽样（CARS），蒙特卡洛非信息变量消除（MCUVE）和迭代保留信息变量（IRIV）等一些有希望的变量选择方法相比，VISSA对NIR数据的校准具有更好的预测能力。另外，VISSA是用户友好的。只需要几个不敏感的参数，程序将自动终止，而无需任何其他条件。可在以下网站上免费获得用于实施VISSA的Matlab代码：https：//sourceforge.net/projects/multivariateanalysis/files/VISSA/。

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