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AUTOMATED, OBJECTIVE AND OPTIMIZED FEATURE SELECTION IN CHEMOMETRIC MODELING (CLUSTER RESOLUTION)

机译:化学建模中的自动,目标和优化特征选择(群集分辨率)

摘要

A novel metric, termed cluster resolution, which compares the separation of clusters of data points while simultaneously considering the shapes of the clusters and their relative orientations. This metric, in conjunction with an objective variable ranking metric, allows for the fully-automated determination of the optimal number of variables to be included in a chemometric model of a system. Cluster resolution is based upon considering the minimum distance between (or the extent of overlap of) confidence ellipses constructed around clusters of points representing different classes of objects. This approach can be generally applied to feature selection for a variety of applications and represents a significant step towards the development of fully-automated, objective construction of chemometric models.
机译:一种称为簇分辨率的新颖度量,它比较数据点的簇的间距,同时考虑簇的形状及其相对方向。该度量标准与客观变量排名度量标准结合使用,可以全自动确定要包含在系统化学计量模型中的变量的最佳数量。聚类分辨率基于考虑围绕代表不同类别的对象的点的聚类构造的置信椭圆之间的最小距离(或重叠程度)。这种方法通常可以应用于各种应用程序的特征选择,并且代表了向化学计量模型的全自动,客观构造的发展迈出的重要一步。

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