Items related to Proactive Data Mining with Decision Trees (SpringerBriefs...

Proactive Data Mining with Decision Trees (SpringerBriefs in Electrical and Computer Engineering) - Softcover

Book 50 of 209: SpringerBriefs in Electrical and Computer Engineering

Dahan, Haim; Cohen, Shahar; Rokach, Lior; Maimon, Oded

 
9781493905386: Proactive Data Mining with Decision Trees (SpringerBriefs in Electrical and Computer Engineering)
Softcover
ISBN 10: 1493905384 ISBN 13: 9781493905386
Publisher: Springer, 2014

Synopsis

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.

"synopsis" may belong to another edition of this title.

From the Back Cover

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.

"About this title" may belong to another edition of this title.

Publisher
Springer
Publication date
2014
Language
English
ISBN 10 
1493905384
ISBN 13 
9781493905386
Binding
Paperback
Number of pages
88