Pessimistic cost-sensitive active learning of decision trees for profit maximizing targeting campaigns

Authors:
Lior Rokach;Lihi Naamani;Armin Shmilovici
Affiliations:
Department of Information System Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel 84105;Deutsche Telekom Laboratories at Ben-Gurion University, Ben-Gurion University of the Negev, Beer-Sheva, Israel 84105;Department of Information System Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel 84105
Venue:
Data Mining and Knowledge Discovery
Year:
2008

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Abstract

In business applications such as direct marketing, decision-makers are required to choose the action which best maximizes a utility function. Cost-sensitive learning methods can help them achieve this goal. In this paper, we introduce Pessimistic Active Learning (PAL). PAL employs a novel pessimistic measure, which relies on confidence intervals and is used to balance the exploration/exploitation trade-off. In order to acquire an initial sample of labeled data, PAL applies orthogonal arrays of fractional factorial design. PAL was tested on ten datasets using a decision tree inducer. A comparison of these results to those of other methods indicates PAL's superiority.