Issue |
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
|
|
---|---|---|
Article Number | 02057 | |
Number of page(s) | 10 | |
Section | Information System | |
DOI | https://doi.org/10.1051/e3sconf/202344802057 | |
Published online | 17 November 2023 |
KNN Optimization Using Grid Search Algorithm for Preeclampsia Imbalance Class
1 Doctoral Program of Information System, School of Postgraduate Studies Diponegoro University, 50241 Semarang, Central Java, Indonesia
2 Department of Electrical Engineering, Politeknik Negeri Semarang, 50275 Semarang, Central Java, Indonesia
3 Department of Physics, Faculty of Science and Mathematics, Diponegoro University, 50241 Semarang, Central Java, Indonesia
a) Corresponding author: suk4mtho@students.undip.ac.id
b) Hadiyanto@live.undip.ac.id
c) kurnianingsih@polines.ac.id
The performance of predicted models is greatly affected when the dataset is highly imbalanced and the sample size increases. Imbalanced training data have a major negative impact on performance. Currently, machine learning algorithms continue to be developed so that they can be optimized using various methods to produce the model with the best performance. One way of optimization with apply hyperparameter tuning. In classification, most of the algorithms have hyperparameters. One of the popular hyperparameter methodologies is Grid Search. GridSearch using Cross Validation makes it easy to test each model parameter without having to do manual validation one by one. In this study, we will use a method in hyperparameter optimization, namely Grid Search. The purpose of this study is to find out the best optimization of hyperparameters for two machine learning classification algorithms that are widely used to handle imbalanced data cases. Validation of the experimental results uses the mean cross-validation measurement metric. The experimental results show that the KNN model gets the best value compared to the Decision Tree.
Key words: Machine learning / hyperparameters / grid search
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.