Dahan / Cohen / Rokach Proactive Data Mining with Decision Trees

1. Auflage 2014
ISBN: 978-1-4939-0539-3
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 94 Seiten, eBook

Reihe: SpringerBriefs in Electrical and Computer Engineering

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.

Zielgruppe

Research

Autoren/Hrsg.

Dahan, Haim

Cohen, Shahar

Rokach, Lior

Maimon, Oded

Inhaltsverzeichnis

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.