Bayesian Optimization Machine Learning Models for True and Fake News Classification
- Resource Type
- Conference
- Authors
- Zhao, Gaohua; Song, Shouyou; Lin, Hao; Jiang, Wei
- Source
- 2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Information Technology,Networking,Electronic and Automation Control Conference (ITNEC), 2023 IEEE 6th. 6:1530-1533 Feb, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Machine learning algorithms
Monte Carlo methods
Optimization methods
Machine learning
Forestry
Bayes methods
Task analysis
Bayesian
machine learning
hyperparameter
KNN
Random Forest
GBDT
- Language
- ISSN
- 2693-3128
The performance of a machine learning algorithm depends largely on determining a set of hyperparameters. These hyperparameters have a significant influence on the accuracy of the algorithm. With the increase in algorithm complexity, there are more and more candidates for hyperparameters. How to quickly and accurately select the right hyperparameters for a given problem has become a popular area of research. This paper is based on a Bayesian optimization approach to assist machine learning for hyperparameter extraction. It is also fully validated based on the task of dichotomous classification of true and false news. This paper analyses the principles of the Bayesian optimization approach and how it can be applied to machine learning model parameter selection. The machine learning models to be used in this paper include K-Nearest Neighbour (KNN), Random Forest as well as Gradient Boosted Decision Trees (GBDT). These three are commonly used machine learning models for binary classification problems, with different numbers and classes of hyperparameters. The results of the experiments show that adjusting the original hyperparameters of machine learning using Bayesian optimization can substantially improve classification accuracy. The research in this paper can also provide ideas for other similar work of super parameter selection.