In many predictive modeling tasks, one has a fixed set of
observations from which a vast, or even infinite, set of
potentially predictive features can be computed. Of these features,
often only a small number are expected to be useful in a predictive
model. Models which use the entire set of features will almost
certainly overfit on future data sets. The book presents streamwise
feature selection which interleaves the process of generating new
features with that of feature testing. Streamwise feature selection
scales well to large feature sets. The book also describes how to
use streamwise feature seleciton in multivariate regressions. It
includes a review of traditional feature selecitions in a general
framework based on information theory, and compares these methods
with streamwise feature selection on various real and synthetic
data sets. This book is intended to be used by researchers in
machine learning, data mining, and knowledge discovery.
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