Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods [electronic resource] / by Sarah Vluymans.
- 作者： Vluymans, Sarah.
- 其他作者：
- 其他題名：
- Studies in computational intelligence ;
- 出版： Cham : Springer International Publishing :Imprint: Springer 2019.
- 叢書名： Studies in computational intelligence,v.807
- 主題： Fuzzy sets. , Computational Intelligence. , Artificial Intelligence.
- ISBN： 9783030046637 (electronic bk.) 、 9783030046620 (paper)
- URL：
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電子書
- 一般註：Introduction -- Classification -- Understanding OWA based fuzzy rough sets -- Fuzzy rough set based classification of semi-supervised data -- Multi-instance learning -- Multi-label learning -- Conclusions and future work -- Bibliography. E1084學校採購電子書
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讀者標籤：
- 系統號： 000274282 | 機讀編目格式
館藏資訊
This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning. The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.
摘要註
This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning. The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.