title: Unsupervised forward selection: a method for eliminating redundant variables.
abstract: An unsupervised learning method is proposed for variable selection and its performance assessed using three typical QSAR data sets. The aims of this procedure are to generate a subset of descriptors from any given data set in which the resultant variables are relevant, redundancy is eliminated, and multicollinearity is reduced. Continuum regression, an algorithm encompassing ordinary least squares regression, regression on principal components, and partial least squares regression, was used to construct models from the selected variables. The variable selection routine is shown to produce simple, robust, and easily interpreted models for the chosen data sets.
authors.affiliation: Centre for Molecular Design, Institute of Biomedical and Biomolecular Science, University of Portsmouth, UK. david.whitley@port.ac.uk
authors.forename: D C
authors.initials: DC
authors.lastname: Whitley
authors.forename: M G
authors.initials: MG
authors.lastname: Ford
authors.forename: D J
authors.initials: DJ
authors.lastname: Livingstone
mesh_terms: D000465:Algorithms; D015195:Drug Design; D008958:Models, Molecular; D011722:Pyrethrins / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship; D013256:Steroids / Q000737:chemistry
chemical_list: D011722:Pyrethrins; D013256:Steroids