| 書(shū)目名稱 | Proactive Data Mining with Decision Trees | | 編輯 | Haim Dahan,Shahar Cohen,Oded Maimon | | 視頻video | http://file.papertrans.cn/757/756772/756772.mp4 | | 概述 | Includes supplementary material: | | 叢書(shū)名稱 | SpringerBriefs in Electrical and Computer Engineering | | 圖書(shū)封面 |  | | 描述 | 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. | | 出版日期 | Book 2014 | | 關(guān)鍵詞 | active learning; data mining; decision trees; maximal-utility splitting criterion; optimization | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4939-0539-3 | | isbn_softcover | 978-1-4939-0538-6 | | isbn_ebook | 978-1-4939-0539-3Series ISSN 2191-8112 Series E-ISSN 2191-8120 | | issn_series | 2191-8112 | | copyright | The Author(s) 2014 |
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