An aggressive driving state recognition model using EEG based on stacking ensemble learning

Yang, Liu; Zhao, Qianxi

doi:10.1080/19439962.2023.2204843

Cited by 5 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where wi is the ith training instance weight and n is the number of training instances. (v) Stacking ensemble method: Stacking is a method of integrating predictions from various machine learning models into the same dataset, such as bagging and boosting [33]. The stacking technique's architecture consists of two or more models, known as base models or level-0, and meta-models that combine the predictions of the base models, known as level-1 models [34].…”

Section: Building the Two-layer Ensemble Modelmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm

Oyoo,

Wekesa,

Ogada

2024

ASI

View full text Add to dashboard Cite

Road traffic collisions are among the world’s critical issues, causing many casualties, deaths, and economic losses, with a disproportionate burden falling on developing countries. Existing research has been conducted to analyze this situation using different approaches and techniques at different stretches and intersections. In this paper, we propose a two-layer ensemble machine learning (ML) technique to assess and predict road traffic collisions using data from a driving simulator. The first (base) layer integrates supervised learning techniques, namely k- Nearest Neighbors (k-NN), AdaBoost, Naive Bayes (NB), and Decision Trees (DT). The second layer predicts road collisions by combining the base layer outputs by employing the stacking ensemble method, using logistic regression as a meta-classifier. In addition, the synthetic minority oversampling technique (SMOTE) was performed to handle the data imbalance before training the model. To simplify the model, the particle swarm optimization (PSO) algorithm was used to select the most important features in our dataset. The proposed two-layer ensemble model had the best outcomes with an accuracy of 88%, an F1 score of 83%, and an AUC of 86% as compared with k-NN, DT, NB, and AdaBoost. The proposed two-layer ensemble model can be used in the future for theoretical as well as practical applications, such as road safety management for improving existing conditions of the road network and formulating traffic safety policies based on evidence.

show abstract

Section: Building the Two-layer Ensemble Modelmentioning

confidence: 99%

Section: Building the Two-layer Ensemble Modelmentioning

confidence: 99%

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm

Oyoo,

Wekesa,

Ogada

2024

ASI

View full text Add to dashboard Cite

show abstract

“…(v) Stacking ensemble method: Stacking is a method of integrating predictions from various machine learning models on the same dataset, such as bagging and boosting [33]. The stacking technique's architecture consists of two or more models, known as base models or level-0, and metamodels that combine the predictions of the base models, known as level-1 models [34].…”

Section: Building the Two-layer Ensemble Modelmentioning

confidence: 99%

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm

Oyoo,

Wekesa,

Ogada

2023

Preprint

View full text Add to dashboard Cite

Road collisions are among the world’s critical issues causing many casualties, deaths, and economic losses. To deal with this scenario, precise analysis is required. In this paper, we use Machine Learning (ML) techniques to assess and predict road traffic collisions. An ensemble machine learning model with two layers was developed to analyze simulation data from the driving simulator. The first (base) layer integrates supervised learning techniques namely, k- Nearest Neighbors (k-NN) AdaBoost, Naive Bayes (NB), and Decision Trees (DT). The second layer predicts road collisions by combining the base layer outputs by employing the stacking ensemble method using logistic regression as a meta-classifier. The data for this study were imbalanced; therefore, we performed a SMOTE resampling strategy to handle the dataset imbalance. To simplify the model, the PSO algorithm was used to select the most important features in our dataset. The two-layer ensemble model proposed had the best outcomes with an accuracy of 88%, F1 score of 83%, and an AUC of 86% as compared with k-NN, DT, NB, and AdaBoost.

show abstract

A novel hybrid fast Fourier transform processor in 5G+ and bio medical applications

Priyadharsini,

Sasipriya

2024

Microprocessors and Microsystems

View full text Add to dashboard Cite

An aggressive driving state recognition model using EEG based on stacking ensemble learning

Cited by 5 publications

References 39 publications

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm

A novel hybrid fast Fourier transform processor in 5G+ and bio medical applications

Contact Info

Product

Resources

About