Neural Message Passing for Multi-Label Classification
This addresses the problem of efficiently predicting multiple labels with complex interactions for applications in domains like text and image classification, representing a novel method rather than an incremental improvement.
The paper tackles the challenge of modeling combinatorial label interactions in multi-label classification by proposing Label Message Passing (LaMP) Neural Networks, which treat labels as nodes on a graph and use attention-based message passing to learn implicit interactions, achieving state-of-the-art results on seven real-world datasets.
Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample. Modeling the combinatorial label interactions in MLC has been a long-haul challenge. We propose Label Message Passing (LaMP) Neural Networks to efficiently model the joint prediction of multiple labels. LaMP treats labels as nodes on a label-interaction graph and computes the hidden representation of each label node conditioned on the input using attention-based neural message passing. Attention enables LaMP to assign different importance to neighbor nodes per label, learning how labels interact (implicitly). The proposed models are simple, accurate, interpretable, structure-agnostic, and applicable for predicting dense labels since LaMP is incredibly parallelizable. We validate the benefits of LaMP on seven real-world MLC datasets, covering a broad spectrum of input/output types and outperforming the state-of-the-art results. Notably, LaMP enables intuitive interpretation of how classifying each label depends on the elements of a sample and at the same time rely on its interaction with other labels. We provide our code and datasets at https://github.com/QData/LaMP