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


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

Animals, vol.14, no.14, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 14
  • Publication Date: 2024
  • Doi Number: 10.3390/ani14142021
  • Journal Name: Animals
  • Journal Indexes: 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
  • Keywords: animals, Binary Relevance, federate learning, Federated Multi-Label Learning, machine learning, multi-label learning, Reduced-Error Pruning Tree
  • Dokuz Eylül University Affiliated: Yes

Abstract

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.