Journal article
Hierarchy-guided neural network for species classification
Methods in ecology and evolution, v 13(3), pp 642-652
05 Dec 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Species classification is an important task which is the foundation of industrial, commercial, ecological and scientific applications involving the study of species distributions, dynamics and evolution. While conventional approaches for this task use off-the-shelf machine learning (ML) methods such as existing Convolutional Neural Network (ConvNet) architectures, there is an opportunity to inform the ConvNet architecture using our knowledge of biological hierarchies among taxonomic classes. In this work, we propose a new approach for species classification termed hierarchy-guided neural network (HGNN), which infuses hierarchical taxonomic information into the neural network's training to guide the structure and relationships among the extracted features. We perform extensive experiments on an illustrative use-case of classifying fish species to demonstrate that HGNN outperforms conventional ConvNet models in terms of classification accuracy, especially under scarce training data conditions. We also observe that HGNN shows better resilience to adversarial occlusions, when some of the most informative patch regions of the image are intentionally blocked and their effect on classification accuracy is studied.
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Details
- Title
- Hierarchy-guided neural network for species classification
- Creators
- Mohannad Elhamod - Virginia TechKelly M. Diamond - Seattle Children's Research InstituteA. Murat Maga - Seattle Children's Research InstituteYasin Bakis - Tulane UniversityHenry L. Bart - Tulane UniversityPaula Mabee - National Ecological Observatory NetworkWasila Dahdul - University of California, IrvineJeremy Leipzig - Drexel UniversityJane Greenberg - Drexel UniversityBrian Avants - University of VirginiaAnuj Karpatne - Virginia Tech
- Publication Details
- Methods in ecology and evolution, v 13(3), pp 642-652
- Publisher
- Wiley
- Number of pages
- 11
- Grant note
- TG-DEB200005 / XSEDE 1940322; 1940233; 2022042; 1940247; 1939505 / National Science Foundation through Harnessing the Data Revolution Ideas Lab program awards
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science; Computer Science
- Web of Science ID
- WOS:000726522200001
- Scopus ID
- 2-s2.0-85120451376
- Other Identifier
- 991019168579204721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Ecology