Welcome to the Unsupervised Forward Selection (UFS)

Unsupervised Forward Selection (UFS) is a data reduction algorithm that selects from a data matrix a maximal linearly independent set of columns with a minimal amount of multiple correlation.UFS was designed for use in the development of Quantitative Structure-Activity Relationship (QSAR) models, where the m by n data matrix contains the values of n variables (typically molecular properties) for m objects (typically compounds). QSAR data sets often contain redundancy (exact linear dependencies between subsets of the variables), and multicollinearity (high multiple correlations between subsets of the variables). Both of these features inhibit the development of QSAR models with the ability to generalise successfully to new objects. UFS produces a reduced data set that contains no redundancy and a minimal amount of multicollinearity. The data input format is described here.

1. Whitley D. C., Ford M. G. and Livingstone D. J., Unsupervised Forward Selection: A Method for Eliminating Redundant Variables, J Chem Inf Comput Sci, 2000, 40, 1160-1168.

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