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

• Autores: Lior Rokach, Lihi Naamani, Armin Shmilovici
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 17, Nº 2, 2008, págs. 283-316
• Idioma: inglés
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• Resumen

  • 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.