  Recently, machine learning algorithms have successfully entered large-scale
real-world industrial applications (e.g. search engines and email spam
filters). Here, the CPU cost during test time must be budgeted and accounted
for. In this paper, we address the challenge of balancing the test-time cost
and the classifier accuracy in a principled fashion. The test-time cost of a
classifier is often dominated by the computation required for feature
extraction-which can vary drastically across eatures. We decrease this
extraction time by constructing a tree of classifiers, through which test
inputs traverse along individual paths. Each path extracts different features
and is optimized for a specific sub-partition of the input space. By only
computing features for inputs that benefit from them the most, our cost
sensitive tree of classifiers can match the high accuracies of the current
state-of-the-art at a small fraction of the computational cost.
