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

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.