{"title":"基于特征选择的交通事故伤害严重程度分析","authors":"Jo-Ting Wei, Hsin-Hung Wu, K. Kou","doi":"10.1109/IJCSS.2011.73","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":251415,"journal":{"name":"2011 International Joint Conference on Service Sciences","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Using Feature Selection to Reduce the Complexity in Analyzing the Injury Severity of Traffic Accidents\",\"authors\":\"Jo-Ting Wei, Hsin-Hung Wu, K. Kou\",\"doi\":\"10.1109/IJCSS.2011.73\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":251415,\"journal\":{\"name\":\"2011 International Joint Conference on Service Sciences\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Joint Conference on Service Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCSS.2011.73\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Joint Conference on Service Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCSS.2011.73","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.