| 書(shū)目名稱(chēng) | Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Metho |
| 編輯 | Sarah Vluymans |
| 視頻video | http://file.papertrans.cn/264/263975/263975.mp4 |
| 概述 | Takes the research on ordered weighted average (OWA) fuzzy rough sets to the next level.Provides clear guidelines on how to use them.Expands the application to e.g. imbalanced, semi-supervised, multi- |
| 叢書(shū)名稱(chēng) | Studies in Computational Intelligence |
| 圖書(shū)封面 |  |
| 描述 | 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.? ?. |
| 出版日期 | Book 2019 |
| 關(guān)鍵詞 | Computational Intelligence; OWA; Ordered Weighted Average; Classification; Multi-Instance Learning; Multi |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-04663-7 |
| isbn_ebook | 978-3-030-04663-7Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer Nature Switzerland AG 2019 |
|Archiver|手機(jī)版|小黑屋|
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