The literature has shown that model ensemble techniques are particularly effective to solve regression/classification applications by providing, given a suitable aggregation mechanism, a better generalization ability than the generic model of the ensemble. However, only few recent results consider the use of ensembles for a time-dependent framework, with focus on time-series forecasting. Here, we propose the use of ensemble of models to an on-line reconstruction of missing data coming from a sensor network. Reconstructing missing data is of paramount importance for any further data processing and must be carried out on-line not to introduce unnecessary latency when data lead to a decision or control action. The ensemble is designed by both exploiting temporal and spatial dependencies existing among the sensors composing the network. An effective aggregation mechanism is proposed for the considered models to improve the generalization ability of the ensemble. Results demonstrate the effectiveness of the proposed approach in reconstructing missing data.

Model ensemble for an effective on-line reconstruction of missing data in sensor networks / C. Alippi, S. Ntalampiras, M. Roveri (PROCEEDINGS OF ... INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS). - In: The 2013 International Joint Conference on Neural Networks (IJCNN)[s.l] : IEEE, 2013. - ISBN 9781467361293. - pp. 1-6 (( convegno International Joint Conference on Neural Networks tenutosi a Dallas nel 2013.

Model ensemble for an effective on-line reconstruction of missing data in sensor networks

S. Ntalampiras;M. Roveri
2013

Abstract

The literature has shown that model ensemble techniques are particularly effective to solve regression/classification applications by providing, given a suitable aggregation mechanism, a better generalization ability than the generic model of the ensemble. However, only few recent results consider the use of ensembles for a time-dependent framework, with focus on time-series forecasting. Here, we propose the use of ensemble of models to an on-line reconstruction of missing data coming from a sensor network. Reconstructing missing data is of paramount importance for any further data processing and must be carried out on-line not to introduce unnecessary latency when data lead to a decision or control action. The ensemble is designed by both exploiting temporal and spatial dependencies existing among the sensors composing the network. An effective aggregation mechanism is proposed for the considered models to improve the generalization ability of the ensemble. Results demonstrate the effectiveness of the proposed approach in reconstructing missing data.
systems
Settore INF/01 - Informatica
2013
International Neural Network Society (INNS)
IEEE Computational Intelligence Society (IEEE-CIS)
Book Part (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/615041
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