Multi-objective, Multi-class and Multi-label Data Classification with Class Imbalance : Theory and Practices / by Sanjay Chakraborty, Lopamudra Dey.

Format:

Book

Author/Creator:

Chakraborty, Sanjay.

Contributor:

Dey, Lopamudra.

Series:

Springer Tracts in Nature-Inspired Computing, 2524-5538

Language:

English

Subjects (All):

Computational intelligence.

Artificial intelligence.

Machine learning.

Computational Intelligence.

Artificial Intelligence.

Machine Learning.

Local Subjects:

Computational Intelligence.

Artificial Intelligence.

Machine Learning.

Physical Description:

1 online resource (177 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

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.

Contents:

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.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Chakraborty, Sanjay Multi-Objective, Multi-class and Multi-label Data Classification with Class Imbalance

ISBN:

9789819796229

OCLC:

1493056447