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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2011 International Joint Conference on Service Sciences, IJCSS 2011 |
| Pages | 329-333 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2011 Sep 5 |
| Event | 2011 International Joint Conference on Service Sciences, IJCSS 2011 - Taipei, Taiwan Duration: 2011 May 25 → 2011 May 27 |
Publication series
| Name | Proceedings - 2011 International Joint Conference on Service Sciences, IJCSS 2011 |
|---|
Other
| Other | 2011 International Joint Conference on Service Sciences, IJCSS 2011 |
|---|---|
| Country | Taiwan |
| City | Taipei |
| Period | 11-05-25 → 11-05-27 |
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All Science Journal Classification (ASJC) codes
- Management of Technology and Innovation
Cite this
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Using feature selection to reduce the complexity in analyzing the injury severity of traffic accidents. / Wei, Jo Ting; Wu, Hsin Hung; Kou, Kuang Yang.
Proceedings - 2011 International Joint Conference on Service Sciences, IJCSS 2011. 2011. p. 329-333 5960374 (Proceedings - 2011 International Joint Conference on Service Sciences, IJCSS 2011).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Using feature selection to reduce the complexity in analyzing the injury severity of traffic accidents
AU - Wei, Jo Ting
AU - Wu, Hsin Hung
AU - Kou, Kuang Yang
PY - 2011/9/5
Y1 - 2011/9/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80052246977&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052246977&partnerID=8YFLogxK
U2 - 10.1109/IJCSS.2011.73
DO - 10.1109/IJCSS.2011.73
M3 - Conference contribution
AN - SCOPUS:80052246977
SN - 9780769544212
T3 - Proceedings - 2011 International Joint Conference on Service Sciences, IJCSS 2011
SP - 329
EP - 333
BT - Proceedings - 2011 International Joint Conference on Service Sciences, IJCSS 2011
ER -