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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Main Authors: | , , , , , |
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Format: | Book Chapter |
Language: | English English |
Published: |
Springer
2020
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Subjects: | |
Online Access: | 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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Summary: | 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%. |
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