Paper Info
Title
A semantic-based classification and regression tree approach for modelling complex spatial rules in motor vehicle crashes domain
Abstract
Innovative data mining and knowledge discovery approaches that take advantage of geospatial analysis can be useful for analysis of motor vehicle crashes on regional highway corridors. This study presents an Ontology-based Classification and Regression Tree OCART approach to induct crash rules on regional highway corridors by adding ontological reasoning to Classification and Regression Tree CART decision rules. It develops an ontology-driven geospatial framework to predict the motor vehicles crash severity through the proposed OCART approach. A system prototype has been developed and implemented on a regional highway corridor in order to illustrate and evaluate the proposed method. The results demonstrate the application of ontological reasoning and spatial computation significantly improves the performance of CART crash rules. The proposed method reveals new relationships among crash severities and contributing factors that have been kept implicit in CART decision rules. WIREs Data Mining Knowl Discov 2015, 5:181-194. doi: 10.1002/widm.1152
Year
DOI
Venue
2015
10.1002/widm.1152
Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery
Field
DocType
Volume
Geospatial analysis,Decision rule,Decision tree,Data mining,Crash,Ontology,Computer science,Cart,Knowledge extraction,Artificial intelligence,Machine learning,Computation
Journal
5
Issue
ISSN
Citations 
4
1942-4787
1
PageRank 
References 
Authors
0.35
16
2
Name
Order
Citations
PageRank
Meysam Effati130.72
Abolghasem Sadeghi-Niaraki2296.53