LNEMLC: Label Network Embeddings for Multi-Label Classification
This addresses the challenge of modeling label dependencies in multi-label classification for applications like text or image tagging, though it appears incremental as it builds on existing classifiers.
The paper tackles the problem of multi-label classification by proposing LNEMLC, a method that embeds label networks to capture joint label probabilities, achieving results comparable to state-of-the-art methods with low computational complexity.
Multi-label classification aims to classify instances with discrete non-exclusive labels. Most approaches on multi-label classification focus on effective adaptation or transformation of existing binary and multi-class learning approaches but fail in modelling the joint probability of labels or do not preserve generalization abilities for unseen label combinations. To address these issues we propose a new multi-label classification scheme, LNEMLC - Label Network Embedding for Multi-Label Classification, that embeds the label network and uses it to extend input space in learning and inference of any base multi-label classifier. The approach allows capturing of labels' joint probability at low computational complexity providing results comparable to the best methods reported in the literature. We demonstrate how the method reveals statistically significant improvements over the simple kNN baseline classifier. We also provide hints for selecting the robust configuration that works satisfactorily across data domains.