Samenvatting
Knowledge present in a domain is well expressed as relationships between corresponding concepts. For example, in zoology, animal species form complex hierarchies; in genomics, the different (parts of) molecules are organized in groups and subgroups based on their functions; plants, molecules, and astronomical objects all form complex taxonomies. Nevertheless, when applying supervised machine learning (ML) in such domains, we commonly reduce the complex and rich knowledge to a fixed set of labels, and induce a model shows good generalization performance with respect to these labels. The main reason for such a reductionist approach is the difficulty in eliciting the domain knowledge from the experts. Developing a label structure with sufficient fidelity and providing comprehensive multi-label annotation can be exceedingly labor-intensive in many real-world applications. In this paper, we provide a method for efficient hierarchical knowledge elicitation (HKE) from experts working with high-dimensional data such as images or videos. Our method is based on psychometric testing and active deep metric learning. The developed models embed the high-dimensional data in a metric space where distances are semantically meaningful, and the data can be organized in a hierarchical structure. We provide empirical evidence with a series of experiments on a synthetically generated dataset of simple shapes, and Cifar 10 and Fashion-MNIST benchmarks that our method is indeed successful in uncovering hierarchical structures.
Originele taal-2 | Engels |
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Titel | Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 |
Redacteuren | Christian Bessiere |
Uitgeverij | International Joint Conferences on Artificial Intelligence (IJCAI) |
Pagina's | 5214-5215 |
Aantal pagina's | 2 |
ISBN van elektronische versie | 9780999241165 |
DOI's | |
Status | Gepubliceerd - 2020 |
Bibliografische nota
16 pages, 11 figuresFinanciering
Acknowledgements. The authors acknowledge the financial support from the Natural Science Foundation of China (51275438).