Investigation of some machine learning algorithms in fish age classification

Semra Benzer, Farid Hassanbaki Garabaghi, Homay Danaei Mehr, Recep Benzer

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

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.
Original languageEnglish
Article number106151
Pages (from-to)1-6
Number of pages6
JournalFisheries Research
Volume245
Early online date19 Oct 2021
DOIs
Publication statusPublished - Jan 2022
Externally publishedYes

Keywords

  • Artificial neural networks
  • age
  • Classification
  • fish
  • Decision tree algorithms
  • Naïve Bayes

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