Multi-objective, Multi-class and Multi-label Data Classification with Class Imbalance Theory and Practices /
Chakraborty, Sanjay.
Multi-objective, Multi-class and Multi-label Data Classification with Class Imbalance Theory and Practices / [electronic resource] : by Sanjay Chakraborty, Lopamudra Dey. - 1st ed. 2024. - XVIII, 164 p. 98 illus., 72 illus. in color. online resource. - 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.
9789819796229
Computational intelligence.
Artificial intelligence.
Machine learning.
Computational Intelligence.
Artificial Intelligence.
Machine Learning.
Q342
006.3
Multi-objective, Multi-class and Multi-label Data Classification with Class Imbalance Theory and Practices / [electronic resource] : by Sanjay Chakraborty, Lopamudra Dey. - 1st ed. 2024. - XVIII, 164 p. 98 illus., 72 illus. in color. online resource. - 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.
9789819796229
Computational intelligence.
Artificial intelligence.
Machine learning.
Computational Intelligence.
Artificial Intelligence.
Machine Learning.
Q342
006.3
