Using feature selection to reduce the complexity in analyzing the injury severity of traffic accidents

Jo Ting Wei, Hsin Hung Wu, Kuang Yang Kou

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings - 2011 International Joint Conference on Service Sciences, IJCSS 2011
頁面329-333
頁數5
DOIs
出版狀態Published - 2011 九月 5
事件2011 International Joint Conference on Service Sciences, IJCSS 2011 - Taipei, Taiwan
持續時間: 2011 五月 252011 五月 27

出版系列

名字Proceedings - 2011 International Joint Conference on Service Sciences, IJCSS 2011

Other

Other2011 International Joint Conference on Service Sciences, IJCSS 2011
國家Taiwan
城市Taipei
期間11-05-2511-05-27

All Science Journal Classification (ASJC) codes

  • Management of Technology and Innovation

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  • 引用此

    Wei, J. T., Wu, H. H., & Kou, K. Y. (2011). Using feature selection to reduce the complexity in analyzing the injury severity of traffic accidents. 於 Proceedings - 2011 International Joint Conference on Service Sciences, IJCSS 2011 (頁 329-333). [5960374] (Proceedings - 2011 International Joint Conference on Service Sciences, IJCSS 2011). https://doi.org/10.1109/IJCSS.2011.73