Research on Cost-sensitive Classification Methods for Imbalanced Data

Chen, You

doi:10.1109/caibda53561.2021.00054

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Serafín et al [28] proposed the nonparametric predictive inference model to improve cost-sensitive decision trees. In addition to modifying tree generation, the fusion of active learning with cost-sensitive algorithms can effectively enhance the classification performance of imbalanced data [29]. Although combining costsensitive learning with classification models effectively improves predictive accuracy for classification results, determining misclassification costs still requires substantial effort.…”

Section: Related Workmentioning

confidence: 99%

ReMAHA–CatBoost: Addressing Imbalanced Data in Traffic Accident Prediction Tasks

Li,

Wu,

Bai

et al. 2023

Applied Sciences

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Using historical information from traffic accidents to predict accidents has always been an area of active exploration by researchers in the field of transportation. However, predicting only the occurrence of traffic accidents is insufficient for providing comprehensive information to relevant authorities. Therefore, further classification of predicted traffic accidents is necessary to better identify and prevent potential hazards and the escalation of accidents. Due to the significant disparity in the occurrence rates of different severity levels of traffic accidents, data imbalance becomes a critical issue. To address the challenge of predicting extremely imbalanced traffic accident events, this paper introduces a predictive framework named ReMAHA–CatBoost. To evaluate the effectiveness of ReMAHA–CatBoost, we conducted experiments on the US–Accidents traffic accident dataset, where the class label imbalance reaches up to 91.40 times. The experimental results demonstrate that the proposed model in this paper exhibits exceptional predictive performance in the domain of imbalanced traffic accident prediction.

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Section: Related Workmentioning

confidence: 99%