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A study of feature importance for king salmon health classification with feature selection

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posted on 2025-03-13, 00:41 authored by Yuye Zhang, Fangfang ZhangFangfang Zhang, Paula Casanovas, Bing XueBing Xue, Mengjie ZhangMengjie Zhang, Jane E Symonds
AbstractKing salmon is important for aquaculture in New Zealand, contributing significant economic value. Fish health is a priority for the industry, and the change in the health status of king salmon needs to be accurately detected at the earliest possible stage. Many factors affect the health of king salmon, such as temperature. Identifying the key features that influence health prediction is a crucial step toward achieving this goal. This study utilizes trial data collected by the Cawthron Institute, which includes diverse information on king salmon, such as blood biochemistry and hematology. We explore the data by employing statistical methods and feature selection techniques in machine learning to identify the most relevant features for king salmon health prediction, aiming to classify individuals as healthy or unhealthy with a small number of features. The results show that although the most efficient feature selection techniques on different datasets vary, overall, feature selection approaches can successfully identify relevant and informative features for king salmon health classification. Through the incorporation of a few selected features, the learned classifiers could still achieve statistically equal or better classification performance. This study not only contributes to the understanding of the health indicators of king salmon but also provides crucial insights into health prediction, which will be beneficial to the improvement of the health of king salmon, leading to the development of more effective management strategies for aquaculture.

History

Preferred citation

Zhang, Y., Zhang, F., Casanovas, P., Xue, B., Zhang, M. & Symonds, J. E. (n.d.). A study of feature importance for king salmon health classification with feature selection. Intelligent Marine Technology and Systems, 2(1), 32-. https://doi.org/10.1007/s44295-024-00046-z

Journal title

Intelligent Marine Technology and Systems

Volume

2

Issue

1

Pagination

32

Publisher

Springer Science and Business Media LLC

Publication status

Published online

Online publication date

2024-11-07

ISSN

2948-1953

eISSN

2948-1953

Article number

32

Language

en