Observatorio de I+D+i UPM

Descripción
Predicting failures in a distributed system based on previous events through logistic regression is a standard approach in literature. This technique is not reliable, though, in two situations: in the prediction of rare events, which do not appear in enough proportion for the algorithm to capture, and in environments where there are too many variables, as logistic regression tends to overfit on this situations; while manually selecting a subset of variables to create the model is error- prone. On this paper, we solve an industrial research case that presented this situation with a combination of elastic net logistic regression, a method that allows us to automatically select useful variables, a process of cross-validation on top of it and the application of a rare events prediction technique to reduce computation time. This process provides two layers of cross- validation that automatically obtain the optimal model complexity and the optimal mode l parameters values, while ensuring even rare events will be correctly predicted with a low amount of training instances. We tested this method against real industrial data, obtaining a total of 60 out of 80 possible models with a 90% average model accuracy.
Internacional	Si
Nombre congreso	AIMS 2014, Second International Conference on Artificial Intelligence, Modeling and Simulation
Tipo de participación	960
Lugar del congreso	Madrid, Spain
Revisores	Si
ISBN o ISSN	978-1-4799-7599-0
DOI
Fecha inicio congreso	18/11/2014
Fecha fin congreso	20/11/2014
Desde la página	120
Hasta la página	126
Título de las actas	2014 Second International Conference on Artificial Intelligence, Modelling and Simulation

Esta actividad pertenece a memorias de investigación

• Autor: Juan Carlos Dueñas Lopez (UPM)

• Creador: Grupo de Investigación: Sistemas de tiempo real y arquitectura de servicios telemáticos
• Departamento: Ingeniería de Sistemas Telemáticos
• Centro o Instituto I+D+i: CENTER FOR OPEN MIDDLEWARE (COM)(Centro Tecnológico Mixto)