Multi-Label Feature Selection Using Adaptive and Transformed Relevance
Sadegh Eskandari Sahar Ghassabi
Multi-label learning has emerged as a crucial paradigm in data analysis,
addressing scenarios where instances are associated with multiple class labels
simultaneously. With the growing prevalence of multi-label data across diverse
applications, such as text and image classification, the significance of
multi-label feature selection has become increasingly evident. This paper
presents a novel information-theoretical filter-based multi-label feature
selection, called ATR, with a new heuristic function. Incorporating a
combinations of algorithm adaptation and problem transformation approaches, ATR
ranks features considering individual labels as well as abstract label space
discriminative powers. Our experimental studies encompass twelve benchmarks
spanning various domains, demonstrating the superiority of our approach over
ten state-of-the-art information-theoretical filter-based multi-label feature
selection methods across six evaluation metrics. Furthermore, our experiments
affirm the scalability of ATR for benchmarks characterized by extensive feature
and label spaces. The codes are available at https://github.com/Sadegh28/ATR