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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
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...
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| Format: | e-Book |
| Language: | English |
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Cham :
Springer International Publishing : Imprint: Springer,
2019.
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| Edition: | 1st ed. 2019. |
| Series: | Studies in Computational Intelligence,
807 |
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| Online Access: | Full-text access View in OPAC |
Table of Contents:
- 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.