Multi-objective and Multi-label Classification

This chapter delves into the complexities of multilabel and multi-objective classification, beginning with a discussion on the application of ‘Multi-objective Support Vector Machine (SVM)’ for multiclass classification. It explores advanced techniques for multi-objective and multilabel feature selection, providing a thorough understanding of how to optimize feature sets for complex classification tasks. Additionally, it covers multilabel classification in image analysis and multi-objective optimization in image segmentation, offering detailed explanations and hands-on coding examples using real-world datasets. This comprehensive approach ensures that the readers gain both theoretical knowledge and practical experience in tackling advanced classification challenges.