Using Imprecise Probabilities to Extract Decision Rules via Decision Trees for Analysis of Traffic Accidents
作者： Griselda López , Laura Garach , Joaquín Abellán , Javier G. Castellano , Carlos J. Mantas
DOI: 10.1007/978-3-319-08644-6_30
关键词:
摘要: The main aim of this study is focused on the extraction or obtaining important decision rules (DRs) using trees (DTs) from traffic accidents’ data. These identify patterns related with severity accident. In work, we have incorporated a new split criterion to built in method named Information Root Node Variation (IRNV) used for extracting these DRs. It will be shown that, adding criterion, information obtained improved trough and different rules, some them use variables than ones original method.
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