Hybrid Approaches for Data Classification Utilizing Quantum and Classical Techniques
Gowrishankar Chinnasamy, V Vignesh · 2024
Multilabel classification has emerged as a crucial paradigm in machine learning, addressing the complexities inherent in assigning multiple labels to single instances across various domains, including text categorization, image recognition, and medical diagnostics. This chapter provides a comprehensive exploration of multilabel classification approaches, emphasizing both problem transformation and algorithm adaptation techniques. The discussion includes an analysis of prevalent methods, such as Binary Relevance and Label Powerset, alongside innovative algorithms designed to capture label dependencies more effectively. The chapter examines evaluation metrics tailored for multilabel scenarios, highlighting their significance in measuring model performance amidst challenges such as label imbalance and correlation. By synthesizing contemporary research and applications, this chapter serves as a valuable resource for researchers and practitioners seeking to enhance multilabel classification systems and improve predictive accuracy.