Nearest labelset using double distances for multi-label classification
Nearest labelset using double distances for multi-label classification
Multi-label classification is a type of supervised learning where an instance may belong to multiple labels simultaneously. Predicting each label independently has been criticized for not exploiting any correlation between labels. In this article we propose a novel approach, Nearest Labelset using Double Distances ( NLDD ), that predicts the …