03-12-2012, 01:54 PM
Extracting Actionable Knowledge from Decision Trees (IEEE)
ABSTRACT
Most data mining algorithms and tools stop at discovered customer models, producing
distribution information on customer profiles. Such techniques, when applied to industrial
problems such as customer relationship management (CRM), are useful in pointing out
customers who are likely attritors and customers who are loyal, but they require human experts
to post process the discovered knowledge manually. Most of the post processing techniques
have been limited to producing visualization results and interestingness ranking, but they do not
directly suggest actions that would lead to an increase in the objective function such as profit.
In this paper, we present novel algorithms that suggest actions to change customers from an
undesired status (such as attritors) to a desired one (such as loyal) while maximizing an
objective function: the expected net profit. These algorithms can discover cost effective actions
to transform customers from undesirable classes to desirable ones. The approach we take
integrates data mining and decision making tightly by formulating the decision making problems
directly on top of the data mining results in a post processing step. To improve the
effectiveness of the approach, we also present an ensemble of decision trees which is shown to
be more robust when the training data changes. Empirical tests are conducted on both a
realistic insurance application domain and UCI benchmark data.