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- DescriptionThis book explores a proactive and domain-driven method to classification tasks. This vel proactive approach to data mining t only induces a model for predicting or explaining a phemen, but also utilizes specific problem/domain kwledge to suggest specific actions to achieve optimal changes in the value of the target attribute. In particular, the authors suggest a specific implementation of the domain-driven proactive approach for classification trees. The book centers on the core idea of moving observations from one branch of the tree to ather. It introduces a vel splitting criterion for decision trees, termed maximal-utility, which maximizes the potential for enhancing profitability in the output tree. Two real-world case studies, one of a leading wireless operator and the other of a major security company, are also included and demonstrate how applying the proactive approach to classification tasks can solve business problems. Proactive Data Mining with Decision Trees is intended for researchers, practitioners and advanced-level students.
- Author(s)Haim Dahan,Lior Rokach,Oded Z. Maimon,Shahar Cohen
- PublisherSpringer-Verlag New York Inc.
- Date of Publication15/02/2014
- SubjectComputing: Professional & Programming
- Series TitleSpringerBriefs in Electrical and Computer Engineering
- Place of PublicationNew York
- Country of PublicationUnited States
- ImprintSpringer-Verlag New York Inc.
- Content Note20 black & white illustrations, 41 black & white tables, biography
- Weight166 g
- Width155 mm
- Height235 mm
- Spine5 mm
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