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

Author/creator Dahan, Haim
Other author Cohen, Shahar.
Other author Rokach, Lior.
Other author Maimon, Oded.
Format Electronic
Publication InfoNew York : Springer, [2014]
Descriptionx, 88 pages : charts ; 24 cm.
Supplemental ContentFull text available from Springer Books
Supplemental ContentFull text available from Springer Nature - Springer Computer Science eBooks 2014 English International
Subjects

SeriesSpringerBriefs in electrical and computer engineering
SpringerBriefs in electrical and computer engineering. ^A1055522
Contents Introduction -- Proactive Data Mining: A General Approach -- Proactive Data Mining Using Decision Trees -- Proactive Data Mining in the Real World: Case Studies -- Sensitivity Analysis of Proactive Data Mining -- Conclusions.
Abstract 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.
Bibliography noteIncludes bibliographical references.
Access restrictionAvailable only to authorized users.
Technical detailsMode of access: World Wide Web
Genre/formElectronic books.
LCCN 2014931371
ISBN9781493905386
ISBN1493905384

Availability

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Electronic Resources ✔ Available