Abstract
The main aim of this study is focused on the extraction or obtaining of important decision rules (DRs) using decision trees (DTs) from traffic accidents’ data. These decision rules identify patterns related with the severity of the accident. In this work, we have incorporated a new split criterion to built decision trees in a method named Information Root Node Variation (IRNV) used for extracting these DRs. It will be shown that, with the adding of this criterion, the information obtained from the method is improved trough new and different decision rules, some of them use different variables than the ones obtained with the original method.
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López, G., Garach, L., Abellán, J., Castellano, J.G., Mantas, C.J. (2014). Using Imprecise Probabilities to Extract Decision Rules via Decision Trees for Analysis of Traffic Accidents. In: Cornelis, C., Kryszkiewicz, M., Ślȩzak, D., Ruiz, E.M., Bello, R., Shang, L. (eds) Rough Sets and Current Trends in Computing. RSCTC 2014. Lecture Notes in Computer Science(), vol 8536. Springer, Cham. https://doi.org/10.1007/978-3-319-08644-6_30
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DOI: https://doi.org/10.1007/978-3-319-08644-6_30
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