Creating ensemble classifiers through order and incremental data selection in a stream Application to the online learning of road safety indicators

• Published in: Pattern analysis and applications : PAA Vol. 16; no. 3; pp. 333 - 347
• Main Authors: Saunier, Nicolas, Midenet, Sophie
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2013; Springer; Springer Verlag
• Subjects: Applied sciences; Artificial intelligence; Computer Science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data processing. List processing. Character string processing; Engineering Sciences; Exact sciences and technology; Ground, air and sea transportation, marine construction; Memory organisation. Data processing; Other; Pattern Recognition; Road transportation and traffic; Software; Theoretical Advances; Road safety; Incremental algorithms; Ensemble methods; Traffic management; Costs; Expert; Aggregate model; Time varying system; Active system; Classification; Diagnosis; Learning algorithm; Streaming; Data analysis; Stability; Spatial cognition; Teletraffic; Conceptual analysis; Road signalling; Video recording; Time management; Traffic safety; Road traffic; Differential method; Artificial intelligence; SECURITE ROUTIERE; ALGORITHME

This paper presents an original time-sensitive traffic management application for road safety diagnosis in signalized intersections. Such applications require to deal with data streams that may be subject to concept drift over various time scales. The method for road safety analysis relies on the es...