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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Detaylı Bibliyografya
Yazar: Vluymans, Sarah (Yazar)
Müşterek Yazar: SpringerLink (Online service)
Materyal Türü: e-Kitap
Dil:İngilizce
Baskı/Yayın Bilgisi: Cham : Springer International Publishing : Imprint: Springer, 2019.
Edisyon:1st ed. 2019.
Seri Bilgileri:Studies in Computational Intelligence, 807
Konular:
Computational intelligence.
Artificial intelligence.
Computational Intelligence.
Artificial Intelligence.
Online Erişim:Full-text access
OPAC'ta görüntüle
İçindekiler:
  • 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.

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