Cost-sensitive Multi-class AdaBoost for Understanding Driving Behavior with Telematics

So, Banghee; Boucher, Jean‐Philippe; Valdez, Emiliano A.

doi:10.2139/ssrn.3644708

Cited by 10 publications

(2 citation statements)

References 15 publications

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“…Adaboost (Adaptive Boosting) begins by making predictions on the original dataset that are as straightforward as possible before assigning equal weights to each observation [22]. If the prediction produced using the first learner is erroneous, it gives the incorrectly predicted statement more importance and goes through an iterative loop.…”

Section: Classification Processmentioning

confidence: 99%

Boosting and bagging classification for computer science journal

Putri

Wibawa

Rasyid

et al. 2023

Int. J. Adv. Intell. Informatics

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In recent years, data processing has become an issue across all disciplines. Good data processing can provide decision-making recommendations. Data processing is covered in academic data processing publications, including those in computer science. This topic has grown over the past three years, demonstrating that data processing is expanding and diversifying, and there is a great deal of interest in this area of study. Within the journal, groupings (quartiles) indicate the journal's influence on other similar studies. SCImago provides this category. There are four quartiles, with the highest quartile being 1 and the lowest being 4. There are, however, numerous differences in class quartiles, with different quartile values for the same journal in different disciplines. Therefore, a method of categorization is provided to solve this issue. Classification is a machine-learning technique that groups data based on the supplied label class. Ensemble Boosting and Bagging with Decision Tree (DT) and Gaussian Nave Bayes (GNB) were utilized in this study. Several modifications were made to the ensemble algorithm's depth and estimator settings to examine the influence of adding values on the resultant precision. In the DT algorithm, both variables are altered, whereas, in the GNB algorithm, just the estimator's value is modified. Based on the average value of the accuracy results, it is known that the best algorithm for computer science datasets is GNB Bagging, with values of 68.96%, 70.99%, and 69.05%. Second-place XGBDT has 67.75% accuracy, 67.69% precision, and 67.83 recall. The DT Bagging method placed third with 67.31 percent recall, 68.13 percent precision, and 67.30 percent accuracy. The fourth sequence is the XGBoost GNB approach, which has an accuracy of 67.07%, a precision of 68.85%, and a recall of 67.18%. The Adaboost DT technique ranks in the fifth position with an accuracy of 63.65%, a precision of 64.21 %, and a recall of 63.63 %. Adaboost GNB is the least efficient algorithm for this dataset since it only achieves 43.19 % accuracy, 48.14 % precision, and 43.2% recall. The results are still quite far from the ideal. Hence the proposed method for journal quartile inequality issues is not advised.

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Section: Classification Processmentioning

confidence: 99%

Boosting and bagging classification for computer science journal

Putri

Wibawa

Rasyid

et al. 2023

Int. J. Adv. Intell. Informatics

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“…To better interpret the risk of insured drivers, Huang and Meng [35] introduced a data‐driven binning method into logistic regression and four machine learning techniques. For more relevant references, see Bian et al [36] and So et al [37]. Although these advanced techniques usually outperform traditional models in prediction accuracy, regulators still do not accept them due to their “black box” nature.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and interpretation of driving risks: Automobile claim frequency modeling with telematics data

Gao

Huang

Meng

2022

Statistical Analysis

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With the development of vehicle telematics and data mining technology, usage-based insurance (UBI) has aroused widespread interest from both academia and industry. The extensive driving behavior features make it possible to further understand the risks of insured vehicles, but pose challenges in the identification and interpretation of important ratemaking factors. This study, based on the telematics data of policyholders in China's mainland, analyzes insurance claim frequency of commercial trucks using both Poisson regression and several machine learning models, including regression tree, random forest, gradient boosting tree, XGBoost and neural network. After selecting the best model, we analyze feature importance, feature effects and the contribution of each feature to the prediction from an actuarial perspective. Our empirical study shows that XGBoost greatly outperforms the traditional models and detects some important risk factors, such as the average speed, the average mileage traveled per day, the fraction of night driving, the number of sudden brakes and the fraction of left/right turns at intersections. These features usually have a nonlinear effect on driving risk, and there are complex interactions between features. To further distinguish high−/low-risk drivers, we run supervised clustering for risk segmentation according to drivers' driving habits. In summary, this study not only provide a more accurate prediction of driving risk, but also greatly satisfy the interpretability requirements of insurance regulators and risk management.

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