Hyperparameter tuning of the model for hunger state classification

Razman, Mohd Azraan and Abdul Majeed, Anwar P.P. and Musa, Rabiu Muazu and Taha, Zahari and Susto, Gian Antonio and Mukai, Yukinori (2020) Hyperparameter tuning of the model for hunger state classification. In: Machine Learning in Aquaculture. Machine Learning in Aquaculture. SpringerBriefs in Applied Sciences and Technology . Springer, Singapore, pp. 49-57. ISBN 978-981-15-2236-9

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Abstract

Abstract To increase the classification, the rate of prediction based on existing 2 models requires additional technique or in this case optimizing the model. Hyper3 parameter tuning is an optimization technique that evaluates and adjusts the free 4 parameters that define the behaviour of classifiers. Data sets were classified practical 5 with classifiers like SVM, k-NN, ANN and DA. To further improve the design effi6 ciency, the secondary optimization level called hyperparameter tuning will be further 7 investigated. DA, SVM, k-NN, decision tree (Tree), logistic regression (LR), random 8 forest tree (RF) and neural network (NN) are evaluated. The k-NN provided 96.47% 9 of the test sets with the best reliability in classifications. Bayesian optimization has 10 been used to refine the hyperparameter; hence, standardize Euclidean distancemetric 11 with a k value of one is the ideal hyperparameters which could achieve classification 12 performance of 97.16%.

Item Type:	Book Chapter
Additional Information:	6616/83297
Uncontrolled Keywords:	K-nearest neighbour , Neural network, Hyperparameter tuning
Subjects:	S Agriculture > SH Aquaculture. Fisheries. Angling S Agriculture > SH Aquaculture. Fisheries. Angling > SH151 Aquaculture - Fish Culture
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Science > Department of Marine Science Kulliyyah of Science
Depositing User:	Dr. Yukinori Mukai
Date Deposited:	25 Sep 2020 15:41
Last Modified:	07 Oct 2020 15:03
URI:	http://irep.iium.edu.my/id/eprint/83297

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