Minimal Learning Machine for Multi-Label Learning
Joonas Hämäläinen Amauri Souza César L. C. Mattos João P. P. Gomes Tommi Kärkkäinen
Distance-based supervised method, the minimal learning machine, constructs a
predictive model from data by learning a mapping between input and output
distance matrices. In this paper, we propose methods and evaluate how this
technique and its core component, the distance mapping, can be adapted to
multi-label learning. The proposed approach is based on combining the distance
mapping with an inverse distance weighting. Although the proposal is one of the
simplest methods in the multi-label learning literature, it achieves
state-of-the-art performance for small to moderate-sized multi-label learning
problems. Besides its simplicity, the proposed method is fully deterministic
and its hyper-parameter can be selected via ranking loss-based statistic which
has a closed form, thus avoiding conventional cross-validation-based
hyper-parameter tuning. In addition, due to its simple linear distance
mapping-based construction, we demonstrate that the proposed method can assess
predictions' uncertainty for multi-label classification, which is a valuable
capability for data-centric machine learning pipelines.