A Simple Method of Solution For Multi-label Feature Selection

Multi-label classification problems suffer from an exponentially large label space, making feature selection computationally expensive. This paper proposes a two-step approach: first compress the label space into a lower-dimensional representation, then run feature selection within that reduced space. The method significantly cuts computational cost while retaining predictive accuracy on high-dimensional benchmark datasets.

Recommended citation: Valadi, Jayaraman K., Prasad T. Ovhal, and Kunal J. Rathore. "A simple method of solution for multi-label feature selection." 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2019. https://ieeexplore.ieee.org/document/8869493