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Multi-objective, multi-class and multi-label data classification with class imbalancetheory and practices /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multi-objective, multi-class and multi-label data classification with class imbalanceby Sanjay Chakraborty, Lopamudra Dey.
Reminder of title:	theory and practices /
Author:	Chakraborty, Sanjay.
other author:	Dey, Lopamudra.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	xviii, 164 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Classification rule mining.
Online resource:	https://doi.org/10.1007/978-981-97-9622-9
ISBN:	9789819796229$q(electronic bk.)

Multi-objective, multi-class and multi-label data classification with class imbalancetheory and practices /
Chakraborty, Sanjay.

Multi-objective, multi-class and multi-label data classification with class imbalancetheory and practices /[electronic resource] :by Sanjay Chakraborty, Lopamudra Dey. - Singapore :Springer Nature Singapore :2024. - xviii, 164 p. :ill. (chiefly color), digital ;24 cm. - Springer tracts in nature-inspired computing,2524-5538. - Springer tracts in nature-inspired computing..

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.

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.

ISBN: 9789819796229$q(electronic bk.)

Standard No.: 10.1007/978-981-97-9622-9doiSubjects--Topical Terms:

758188
Classification rule mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Multi-objective, multi-class and multi-label data classification with class imbalancetheory and practices /
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505 0 $a 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.
520 $a 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.
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