摘要：For many classi cation and regression problems, a large number of
features are available for possible use | this is typical of DNA microarray data
on gene expression, for example. Often, for computational or other reasons, only
a small subset of these features are selected for use in a model, based on some
simple measure such as correlation with the response variable. This procedure
may introduce an optimistic bias, however, in which the response variable appears
to be more predictable than it actually is, because the high correlation of the
selected features with the response may be partly or wholly due to chance. We
show how this bias can be avoided when using a Bayesian model for the joint
distribution of features and response. The crucial insight is that even if we forget
the exact values of the unselected features, we should retain, and condition on, the
knowledge that their correlation with the response was too small for them to be
selected. In this paper we describe how this idea can be implemented for \naive
Bayes" models of binary data. Experiments with simulated data con rm that this
method avoids bias due to feature selection. We also apply the naive Bayes model
to subsets of data relating gene expression to colon cancer, and nd that correcting
for bias from feature selection does improve predictive performance.
关键词：feature selection, optimistic bias, naive Bayes models, gene expression
data
