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Abstract : This paper aims at formalizing the concept of learning rules from multisource data in a cardiac monitoring context. Our method has been implemented and evaluated on learning from data describing cardiac behaviors from different viewpoints, here electrocardiograms and arterial blood pressure measures. In order to cope with the dimensionality problems of multisource learning, we propose an Inductive Logic Programming method using a two-step strategy. Firstly, rules are learned independently from each sources. Secondly, the learned rules are used to bias a new learning process from the aggregated data. The results show that the the proposed method is much more efficient than learning directly from the aggregated data. Furthermore, it yields rules having better or equal accuracy than rules obtained by monosource learning
https://hal.inria.fr/inria-00000184
Contributor : Elisa Fromont
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Submitted on : Friday, July 29, 2005 - 11:37:03 AM
Last modification on : Thursday, November 15, 2018 - 11:57:04 AM
Document(s) archivé(s) le : Thursday, April 1, 2010 - 10:09:27 PM
Elisa Fromont, René Quiniou, Marie-Odile Cordier. Learning Rules from Multisource Data for Cardiac Monitoring. Artificial Intelligence in Medicine, Jul 2005, Aberdeen, Scotland, pp.484-493. ⟨inria-00000184⟩
