1. Federated Multi-Label Learning (FMLL): Innovative Method for Classification Tasks in Animal Science.
- Author
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Ghasemkhani, Bita, Varliklar, Ozlem, Dogan, Yunus, Utku, Semih, Birant, Kokten Ulas, and Birant, Derya
- Subjects
FEDERATED learning ,ANIMAL classification ,MACHINE learning ,ANIMAL populations ,TREE pruning - Abstract
Simple Summary: This study addresses the classification task in animal science, which helps organize and analyze complex data, essential for making informed decisions. It introduces Federated Multi-Label Learning (FMLL), a novel approach combining federated learning principles with a multi-label learning technique. Using machine learning strategies, FMLL achieved significant improvements in classification accuracy metrics compared to existing methods. The experimental results on different animal datasets demonstrated the effectiveness of FMLL and its superiority in multi-label classification tasks. The findings of our study offer valuable insights into understanding and managing animal populations, which could have important implications for biodiversity conservation and ecological management. Federated learning is a collaborative machine learning paradigm where multiple parties jointly train a predictive model while keeping their data. On the other hand, multi-label learning deals with classification tasks where instances may simultaneously belong to multiple classes. This study introduces the concept of Federated Multi-Label Learning (FMLL), combining these two important approaches. The proposed approach leverages federated learning principles to address multi-label classification tasks. Specifically, it adopts the Binary Relevance (BR) strategy to handle the multi-label nature of the data and employs the Reduced-Error Pruning Tree (REPTree) as the base classifier. The effectiveness of the FMLL method was demonstrated by experiments carried out on three diverse datasets within the context of animal science: Amphibians, Anuran-Calls-(MFCCs), and HackerEarth-Adopt-A-Buddy. The accuracy rates achieved across these animal datasets were 73.24%, 94.50%, and 86.12%, respectively. Compared to state-of-the-art methods, FMLL exhibited remarkable improvements (above 10%) in average accuracy, precision, recall, and F-score metrics. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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