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Extracting Actionable Knowledge from Decision Trees
January 2007 (vol. 19 no. 1)
pp. 43-56
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 postprocess the discovered knowledge manually. Most of the postprocessing 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 postprocessing 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.

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Index Terms:
Phrases decision making, data mining, machine learning.
Citation:
Qiang Yang, Jie Yin, Charles Ling, Rong Pan, "Extracting Actionable Knowledge from Decision Trees," IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 1, pp. 43-56, Jan. 2007, doi:10.1109/TKDE.2007.10
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