E-Book, Englisch, 164 Seiten, eBook
Chakraborty / Dey Multi-objective, Multi-class and Multi-label Data Classification with Class Imbalance
1. Auflage 2024
ISBN: 978-981-97-9622-9
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
Theory and Practices
E-Book, Englisch, 164 Seiten, eBook
Reihe: Springer Tracts in Nature-Inspired Computing
ISBN: 978-981-97-9622-9
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book explores intricate world of data classification with 'Multi-Objective, Multi-Class, and Multi-Label Data Classification.' This book studies sophisticated methods and strategies for working with complicated data sets, tackling the difficulties of various classes, many objectives, and complicated labelling tasks. This resource fosters a deeper grasp of multi-dimensional data analysis in today's data-driven world by providing readers with the skills and insights needed to navigate the subtleties of modern classification jobs, from algorithmic techniques to practical applications.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
1. Introduction to Classification.- 2. Class Imbalance and Data Irregularities in Classification.- 3. Multi-class Classification.- 4. Multi-Objective and Multi-Label Classification.- 5. Deep Learning Inspired Multiclass and Multilabel Classification.- 6. Applications of Multi-objective, Multi-label and Multi-class Classifications.