Abstract
Abalone is a marine snail found in the cold coastal regions. Age is a vital characteristic that is used to determine its worth. Currently, the only viable solution to determine the age of abalone is through very detailed steps in a laboratory. This paper exploits various machine learning models for determining its age. A comprehensive analysis of various machine learning algorithms for abalone age prediction is performed which include, backpropagation feed-forward neural network (BPFFNN), K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, Random Forest, Gauss Naive Bayes, and Support Vector Machine (SVM). In addition, five different optimizers were also tested with BPFFNN to evaluate their effect on its performance. Comprehensive experiments were performed using our data set.
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Guney, S., Kilinc, I., Hameed, A.A., Jamil, A. (2022). Abalone Age Prediction Using Machine Learning. In: Djeddi, C., Siddiqi, I., Jamil, A., Ali Hameed, A., Kucuk, İ. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2021. Communications in Computer and Information Science, vol 1543. Springer, Cham. https://doi.org/10.1007/978-3-031-04112-9_25
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DOI: https://doi.org/10.1007/978-3-031-04112-9_25
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