Deflating Dataset Bias Using Synthetic Data Augmentation

Jaipuria, Nikita; Zhang, Xianling; Bhasin, Rohan; Arafa, M.; Chakravarty, Punarjay; Shrivastava, Shubham; Manglani, Sagar; Murali, Vidya N.

doi:10.48550/arxiv.2004.13866

2020

DOI: 10.48550/arxiv.2004.13866

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Deflating Dataset Bias Using Synthetic Data Augmentation

Nikita Jaipuria¹,

Xianling Zhang²,

Rohan Bhasin³

et al.

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“…The importance of the ratio of synthetic data in the training mix was demonstrated by Jaipuria et al, who found that incorporating 40-60% synthetic data significantly enhances the model's precision [47]. Adopting a similar approach in our study, using the integrated tire-vehicle-pavement modeling approach developed by Liu et al [48] could lead to further improvements in the model's performance.…”

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confidence: 51%

Application of Machine Learning Models to the Analysis of Skid Resistance Data

Koné,

Es-Sabar,

2023

Lubricants

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This paper evaluates the ability of some state-of-the-art Machine Learning models, namely SVM (support vector machines), DT (decision tree) and MLR (multiple linear regression), to predict pavement skid resistance. The study encompasses both regression and classification tasks. In the regression task, the aim is to predict the coefficient of friction values, while the classification task seeks to identify three classes of skid resistance: good, intermediate and bad. The dataset used in this work was gathered through an extensive test campaign that involved a fifth-wheel device to measure the coefficient of friction at different slip ratios on different road surfaces, vehicle speeds, tire tread depths and water depths. It was found that the RBF-SVM model, due to its ability to capture non-linear relationships between the features and the target for a relatively small dataset, is the most adapted tool compared with, on one side, MLR, linear SVM and DT models for the regression task and, on the other side, linear SVM and DT models for the classification task. The paper also discusses the strengths and weaknesses of the investigated models based on the underlying physical phenomena related to skid resistance.

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supporting

confidence: 51%

Application of Machine Learning Models to the Analysis of Skid Resistance Data

Koné,

Es-Sabar,

2023

Lubricants

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Deflating Dataset Bias Using Synthetic Data Augmentation

Cited by 1 publication

References 0 publications

Application of Machine Learning Models to the Analysis of Skid Resistance Data

Application of Machine Learning Models to the Analysis of Skid Resistance Data

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