TY - CHAP
T1 - Hyperparameter tuning of the model for hunger state classification
AU - Mohd Razman, Mohd Azraai
AU - P. P. Abdul Majeed, Anwar
AU - Muazu Musa, Rabiu
AU - Taha, Zahari
AU - Susto, Gian Antonio
AU - Mukai, Yukinori
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2020.
PY - 2020
Y1 - 2020
N2 - To increase the classification, the rate of prediction based on existing models requires additional technique or in this case optimizing the model. Hyperparameter tuning is an optimization technique that evaluates and adjusts the free parameters that define the behaviour of classifiers. Data sets were classified practical with classifiers like SVM, k-NN, ANN and DA. To further improve the design efficiency, the secondary optimization level called hyperparameter tuning will be further investigated. DA, SVM, k-NN, decision tree (Tree), logistic regression (LR), random forest tree (RF) and neural network (NN) are evaluated. The k-NN provided 96.47% of the test sets with the best reliability in classifications. Bayesian optimization has been used to refine the hyperparameter; hence, standardize Euclidean distance metric with a k value of one is the ideal hyperparameters which could achieve classification performance of 97.16%.
AB - To increase the classification, the rate of prediction based on existing models requires additional technique or in this case optimizing the model. Hyperparameter tuning is an optimization technique that evaluates and adjusts the free parameters that define the behaviour of classifiers. Data sets were classified practical with classifiers like SVM, k-NN, ANN and DA. To further improve the design efficiency, the secondary optimization level called hyperparameter tuning will be further investigated. DA, SVM, k-NN, decision tree (Tree), logistic regression (LR), random forest tree (RF) and neural network (NN) are evaluated. The k-NN provided 96.47% of the test sets with the best reliability in classifications. Bayesian optimization has been used to refine the hyperparameter; hence, standardize Euclidean distance metric with a k value of one is the ideal hyperparameters which could achieve classification performance of 97.16%.
KW - Bayesian optimization
KW - Classification
KW - Hyperparameter tuning
KW - K-nearest neighbour
KW - Neural network
UR - http://www.scopus.com/inward/record.url?scp=85078294885&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-2237-6_5
DO - 10.1007/978-981-15-2237-6_5
M3 - Chapter
AN - SCOPUS:85078294885
T3 - SpringerBriefs in Applied Sciences and Technology
SP - 49
EP - 57
BT - SpringerBriefs in Applied Sciences and Technology
PB - Springer
ER -