Federated Multi-Label Learning (FMLL): Innovative Method for Classification Tasks in Animal Science

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Atıf İçin Kopyala

Ghasemkhani B., Varliklar O., Dogan Y., Utku S., Birant K. U., Birant D.

Animals, cilt.14, sa.14, 2024 (SCI-Expanded)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 14 Sayı: 14
• Basım Tarihi: 2024
• Doi Numarası: 10.3390/ani14142021
• Dergi Adı: Animals
• Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, CAB Abstracts, EMBASE, Food Science & Technology Abstracts, Veterinary Science Database, Directory of Open Access Journals
• Anahtar Kelimeler: animals, Binary Relevance, federate learning, Federated Multi-Label Learning, machine learning, multi-label learning, Reduced-Error Pruning Tree
• Dokuz Eylül Üniversitesi Adresli: Evet

Özet

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.