Investigation of some machine learning algorithms in fish age classification

BENZER S., Garabaghi F. H. , Benzer R., Mehr H. D.

FISHERIES RESEARCH, vol.245, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 245
  • Publication Date: 2022
  • Doi Number: 10.1016/j.fishres.2021.106151
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Environment Index, Pollution Abstracts, Veterinary Science Database
  • Keywords: Age, Artificial neural networks, Classification, Decision tree algorithms, Fish, Naive Bayes, DECISION TREE, WATER-QUALITY, GROWTH
  • Gazi University Affiliated: Yes


Marine and freshwater scientists use fish scales, vertebrae, otoliths and length-weights values to estimate fish age because reliable fish age estimation plays a very important role in fish stock management. The advances in technology and the widespread use of artificial intelligence have revealed the use of traditional observations and techniques in the fishing industry. The aim of this study was to evaluate the effectiveness of three disesteemed machine learning algorithms (NB, J48 DT, RF) in comparison with ANNs which has been widely used in such studies in the literature. In culmination, all three algorithms outperformed ANNs and can be considered as alternatives in case of coming across noisy and non-linear datasets. Moreover, among these three algorithms J48 DT and RF showed exceptional performance where the data for specific fish age groups weren't abundant.