Hyperparameter tuning of the model for hunger state classification
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 classif...
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my.iium.irep.832972020-10-07T07:03:27Z http://irep.iium.edu.my/83297/ Hyperparameter tuning of the model for hunger state classification Razman, Mohd Azraan Abdul Majeed, Anwar P.P. Musa, Rabiu Muazu Taha, Zahari Susto, Gian Antonio Mukai, Yukinori SH Aquaculture. Fisheries. Angling SH151 Aquaculture - Fish Culture 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%. Springer 2020 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/83297/1/83297_Hyperparameter%20tuning%20of%20the%20model_MYRA.pdf application/pdf en http://irep.iium.edu.my/83297/2/83297_Hyperparameter%20tuning%20of%20the%20model_SCOPUS.pdf 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 https://doi.org/10.1007/978-981-15-2237-6_5 |
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SH Aquaculture. Fisheries. Angling SH151 Aquaculture - Fish Culture Razman, Mohd Azraan Abdul Majeed, Anwar P.P. Musa, Rabiu Muazu Taha, Zahari Susto, Gian Antonio Mukai, Yukinori Hyperparameter tuning of the model for hunger state classification |
description |
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%. |
format |
Book Chapter |
author |
Razman, Mohd Azraan Abdul Majeed, Anwar P.P. Musa, Rabiu Muazu Taha, Zahari Susto, Gian Antonio Mukai, Yukinori |
author_facet |
Razman, Mohd Azraan Abdul Majeed, Anwar P.P. Musa, Rabiu Muazu Taha, Zahari Susto, Gian Antonio Mukai, Yukinori |
author_sort |
Razman, Mohd Azraan |
title |
Hyperparameter tuning of the model for hunger state classification |
title_short |
Hyperparameter tuning of the model for hunger state classification |
title_full |
Hyperparameter tuning of the model for hunger state classification |
title_fullStr |
Hyperparameter tuning of the model for hunger state classification |
title_full_unstemmed |
Hyperparameter tuning of the model for hunger state classification |
title_sort |
hyperparameter tuning of the model for hunger state classification |
publisher |
Springer |
publishDate |
2020 |
url |
http://irep.iium.edu.my/83297/1/83297_Hyperparameter%20tuning%20of%20the%20model_MYRA.pdf http://irep.iium.edu.my/83297/2/83297_Hyperparameter%20tuning%20of%20the%20model_SCOPUS.pdf http://irep.iium.edu.my/83297/ https://doi.org/10.1007/978-981-15-2237-6_5 |
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1680320899234922496 |
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13.15806 |