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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27815
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| Title: | Decision-centric Active Learning of Binary-Outcome Models |
| Authors: | Saar-Tsechansky, Maytal
Provost, Foster |
| Issue Date: | Mar-2007 |
| Publisher: | Information Systems Research |
| Citation: | Information Systems Research 18(1), March 2007, pp. 4-22 |
| Series/Report no.: | CeDER-PP-2007-08 |
| Abstract: | It can be expensive to acquire the data required for businesses to
employ data-driven predictive modeling, for example to model consumer
preferences to optimize targeting. Prior research has introduced
“active learning” policies for identifying data that are
particularly useful for model induction, with the goal of decreasing the
statistical error for a given acquisition cost (error-centric
approaches). However, predictive models are used as part of a
decision-making process, and costly improvements in model accuracy do
not always result in better decisions. This paper introduces a new
approach for active data acquisition that targets decision-making
specifically. The new decision-centric approach departs from traditional
active learning by placing emphasis on acquisitions that are more likely
to affect decision-making. We describe two different types of
decision-centric techniques. Next, using direct-marketing data, we
compare various data-acquisition techniques. We demonstrate that
strategies for reducing statistical error can be wasteful in a
decision-making context, and show that one decision-centric technique in
particular can improve targeting decisions significantly. We also show
that this method is robust in the face of decreasing quality of utility
estimations, eventually converging to uniform random sampling, and that
it can be extended to situations where different data acquisitions have
different costs. The results suggest that businesses should consider
modifying their strategies for acquiring information through normal
business transactions. For example, a firm such as Amazon.com that
models consumer preferences for customized marketing may accelerate
learning by proactively offering recommendations—not merely to
induce immediate sales, but for improving recommendations in the future. |
| URI: | http://hdl.handle.net/2451/27815 |
| Appears in Collections: | CeDER Published Papers
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