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Multi-objective, multi-class and multi-label data classification with class imbalance : theory and practices / Sanjay Chakraborty, Lopamudra Dey

By: Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Springer tracts in nature-inspired computingPublisher: Singapore : Springer, [2024]Copyright date: © 2024Description: 1 Online-Ressource (xviii, 164 Seiten)ISBN:
  • 9789819796229
Subject(s): Additional physical formats: 9789819796212 | 9789819796236 | 9789819796243 | Erscheint auch als: 9789819796212 Druck-Ausgabe | Erscheint auch als: 9789819796236 Druck-Ausgabe | Erscheint auch als: 9789819796243 Druck-AusgabeDDC classification:
  • 006.3 23
DOI: DOI: 10.1007/978-981-97-9622-9Online resources: Summary: 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.Summary: 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.PPN: PPN: 1913347222Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-INR | ZDB-2-SXIT
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