Rule based systems for big data : a machine learning approach

• 作者： Liu, Han, author.
• 其他作者：
  • Gegov, Alexander, author.
  • Cocea, Mihaela, author.
  • SpringerLink (Online service)
• 其他題名：
  • Studies in big data ;
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Studies in big data,volume 13
• 主題： System design. , Rule-based programming. , Machine learning. , Big data. , Engineering. , Computational Intelligence. , Artificial Intelligence (incl. Robotics) , Data Mining and Knowledge Discovery.
• ISBN： 9783319236964 (electronic bk.) 、 9783319236957 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Introduction -- Theoretical Preliminaries -- Generation of Classification Rules -- Simplification of Classification Rules -- Representation of Classification Rules -- Ensemble Learning Approaches -- Interpretability Analysis.
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• 摘要註: The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.
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