TaxoKnow: Taxonomy as Prior Knowledge in the Loss Function of Multi-class Classification

• URL: http://arxiv.org/abs/2305.16341v1
• Date: Wed, 24 May 2023 08:08:56 GMT
• Title: TaxoKnow: Taxonomy as Prior Knowledge in the Loss Function of Multi-class Classification
• Authors: Mohsen Pourvali, Yao Meng, Chen Sheng, Yangzhou Du
• Abstract summary: We introduce two methods to integrate the hierarchical taxonomy as an explicit regularizer into the loss function of learning algorithms. By reasoning on a hierarchical taxonomy, a neural network alleviates its output distributions over the classes, allowing conditioning on upper concepts for a minority class.
• Score: 1.130757825611188
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we investigate the effectiveness of integrating a hierarchical taxonomy of labels as prior knowledge into the learning algorithm of a flat classifier. We introduce two methods to integrate the hierarchical taxonomy as an explicit regularizer into the loss function of learning algorithms. By reasoning on a hierarchical taxonomy, a neural network alleviates its output distributions over the classes, allowing conditioning on upper concepts for a minority class. We limit ourselves to the flat classification task and provide our experimental results on two industrial in-house datasets and two public benchmarks, RCV1 and Amazon product reviews. Our obtained results show the significant effect of a taxonomy in increasing the performance of a learner in semisupervised multi-class classification and the considerable results obtained in a fully supervised fashion.

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