Proactive data mining with decision trees [electronic resource] / Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon.

Proactive data mining with decision trees [electronic resource] / Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon.

9781493905393 electronic book
1493905392 electronic book
9781493905386

New York, NY : Springer, 2014.

English

1 online resource (x, 88 pages) : illustrations.

10.1007/978-1-4939-0539-3 doi

QA76.9.D343

006.312

This book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only induces a model for predicting or explaining a phenomenon, but also utilizes specific problem/domain knowledge 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 another. It introduces a novel 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.

Includes bibliographical references.

Access limited to authorized users.

Description based on online resource; title from PDF title page (SpringerLink, viewed February 17, 2014).

SpringerBriefs in electrical and computer engineering.

Print version; 9781493905386

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