Nearest Labelset Using Double Distances for Multi-label Classification

no code implementations • 15 Feb 2017 • Hyukjun Gweon, Matthias Schonlau, Stefan Steiner

In this paper we propose a novel approach, Nearest Labelset using Double Distances (NLDD), that predicts the labelset observed in the training data that minimizes a weighted sum of the distances in both the feature space and the label space to the new instance.

Classification General Classification +1