  Many applications require the collection of data on different variables or
measurements over many system performance metrics. We term those broadly as
measures or variables. Often data collection along each measure incurs a cost,
thus it is desirable to consider the cost of measures in modeling. This is a
fairly new class of problems in the area of cost-sensitive learning. A few
attempts have been made to incorporate costs in combining and selecting
measures. However, existing studies either do not strictly enforce a budget
constraint, or are not the `most' cost effective. With a focus on
classification problem, we propose a computationally efficient approach that
could find a near optimal model under a given budget by exploring the most
`promising' part of the solution space. Instead of outputting a single model,
we produce a model schedule -- a list of models, sorted by model costs and
expected predictive accuracy. This could be used to choose the model with the
best predictive accuracy under a given budget, or to trade off between the
budget and the predictive accuracy. Experiments on some benchmark datasets show
that our approach compares favorably to competing methods.
