A Similarity-based Framework for Classification Task
This work addresses classification tasks, particularly for multi-label learning, by providing an interpretable method, but it appears incremental as it builds on existing similarity-based approaches.
The authors tackled the problem of classification by generalizing similarity-based methods and integrating them with generalized linear models to capture class interdependencies and handle noisy classes, resulting in a framework that shows effectiveness on multi-class and multi-label datasets.
Similarity-based method gives rise to a new class of methods for multi-label learning and also achieves promising performance. In this paper, we generalize this method, resulting in a new framework for classification task. Specifically, we unite similarity-based learning and generalized linear models to achieve the best of both worlds. This allows us to capture interdependencies between classes and prevent from impairing performance of noisy classes. Each learned parameter of the model can reveal the contribution of one class to another, providing interpretability to some extent. Experiment results show the effectiveness of the proposed approach on multi-class and multi-label datasets