Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Machine Learning - special issue on inductive logic programming
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Learning from multi-source data
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Artificial Intelligence in Medicine
Knowledge construction from time series data using a collaborative exploration system
Journal of Biomedical Informatics
A Human-Machine Cooperative Approach for Time Series Data Interpretation
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Intelligent adaptive monitoring for cardiac surveillance
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
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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.