Hyperparameter tuning of the model for hunger state classification

Mohd Azraai Mohd Razman*, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian Antonio Susto, Yukinori Mukai

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingChapterpeer-review

Abstract

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%.

Original languageEnglish
Title of host publicationSpringerBriefs in Applied Sciences and Technology
PublisherSpringer
Pages49-57
Number of pages9
DOIs
Publication statusPublished - 2020
Externally publishedYes

Publication series

NameSpringerBriefs in Applied Sciences and Technology
ISSN (Print)2191-530X
ISSN (Electronic)2191-5318

Keywords

  • Bayesian optimization
  • Classification
  • Hyperparameter tuning
  • K-nearest neighbour
  • Neural network

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