基于特征选择的交通事故伤害严重程度分析

Jo-Ting Wei, Hsin-Hung Wu, K. Kou
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引用次数: 2

摘要

在对交通事故进行损伤严重程度预测分析时,以往的研究往往使用过多的变量,导致分析结果的过度拟合和解释复杂化。通过特征选择技术,将数据集中不相关和冗余的特征过滤掉,从而提供高判别能力和信息量的特征。本文采用特征选择的方法,选取28个因子对台湾地区交通事故伤害程度进行分析。该方法有助于降低交通事故伤害严重程度分析的复杂性。研究结果表明,19个因素被划分为重要因素,1个被划分为边缘因素,5个被划分为不重要因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Feature Selection to Reduce the Complexity in Analyzing the Injury Severity of Traffic Accidents
When analyzing the traffic accidents in terms of predicting injury severity, past studies often use too many variables and thus lead to over fitting and complicate the interpretation of the analysis. By adopting feature selection technique, irrelevant and redundant features from a dataset will be filtered out such that high discrimination power and informative features will be provided. This paper selects twenty eight factors by adopting feature selection to analyze the injury severity of traffic accidents in Taiwan. The method facilitates to reduce the complexity of analyzing the injury severity of traffic accidents. The findings show that nineteen factors are classified into important, one is categorized as marginal, and five are grouped into unimportant.
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