We are interested in using parallel universes to learn interpretable models that can be subsequen... more We are interested in using parallel universes to learn interpretable models that can be subsequently used to automatically diagnose cardiac arrhythmias. In our study, parallel universes are heterogeneous sources such as electrocardiograms, blood pressure measurements, phonocardio- grams etc. that give relevant information about the cardiac state of a patient. To learn interpretable rules, we use an inductive logic program- ming
This paper gives a comparison of two dif- ferent systems that induce cardiac arrhyth- mia rules b... more This paper gives a comparison of two dif- ferent systems that induce cardiac arrhyth- mia rules by symbolic learning: Kardio and Calicot. In particular, it proposes a de- tailed methodology to compare them and gives some results of this comparison.
This paper gives a comparison of two dif- ferent systems that induce cardiac arrhyth- mia rules b... more This paper gives a comparison of two dif- ferent systems that induce cardiac arrhyth- mia rules by symbolic learning: Kardio and Calicot. In particular, it proposes a de- tailed methodology to compare them and gives some results of this comparison.
This paper formalises the concept of learning symbolic rules from multisource data in a cardiac m... more This paper formalises the concept of learning symbolic rules from multisource data in a cardiac monitoring context. Our sources, electrocardiograms and arterial blood pressure measures, describe cardiac behaviours from different viewpoints. To learn interpretable rules, we use an Inductive Logic Programming (ILP) method. We develop an original strategy to cope with the dimensionality issues caused by using this ILP technique
Abstract. This paper proposes an efficient method to learn from multi source data with an inducti... more Abstract. This paper proposes an efficient method to learn from multi source data with an inductive logic programming method. The method is based on two steps. The first one consists in learning rules independently from each source. In the second step the learnt ...
All currently known algorithms for learning decision trees are based on the paradigm of heuristic... more All currently known algorithms for learning decision trees are based on the paradigm of heuristic top-down induction. Although the results of these algorithms are usually good, there is no guarantee that the resulting trees are really as small, accurate or shallow as possible. In this paper, we introduce an al- gorithm for inducing the smallest most accu- rate decision tree
We are interested in using parallel universes to learn interpretable models that can be subsequen... more We are interested in using parallel universes to learn interpretable models that can be subsequently used to automatically diagnose cardiac arrhythmias. In our study, parallel universes are heterogeneous sources such as electrocardiograms, blood pressure measurements, phonocardio- grams etc. that give relevant information about the cardiac state of a patient. To learn interpretable rules, we use an inductive logic program- ming
This paper gives a comparison of two dif- ferent systems that induce cardiac arrhyth- mia rules b... more This paper gives a comparison of two dif- ferent systems that induce cardiac arrhyth- mia rules by symbolic learning: Kardio and Calicot. In particular, it proposes a de- tailed methodology to compare them and gives some results of this comparison.
This paper gives a comparison of two dif- ferent systems that induce cardiac arrhyth- mia rules b... more This paper gives a comparison of two dif- ferent systems that induce cardiac arrhyth- mia rules by symbolic learning: Kardio and Calicot. In particular, it proposes a de- tailed methodology to compare them and gives some results of this comparison.
This paper formalises the concept of learning symbolic rules from multisource data in a cardiac m... more This paper formalises the concept of learning symbolic rules from multisource data in a cardiac monitoring context. Our sources, electrocardiograms and arterial blood pressure measures, describe cardiac behaviours from different viewpoints. To learn interpretable rules, we use an Inductive Logic Programming (ILP) method. We develop an original strategy to cope with the dimensionality issues caused by using this ILP technique
Abstract. This paper proposes an efficient method to learn from multi source data with an inducti... more Abstract. This paper proposes an efficient method to learn from multi source data with an inductive logic programming method. The method is based on two steps. The first one consists in learning rules independently from each source. In the second step the learnt ...
All currently known algorithms for learning decision trees are based on the paradigm of heuristic... more All currently known algorithms for learning decision trees are based on the paradigm of heuristic top-down induction. Although the results of these algorithms are usually good, there is no guarantee that the resulting trees are really as small, accurate or shallow as possible. In this paper, we introduce an al- gorithm for inducing the smallest most accu- rate decision tree
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Papers by Elisa Fromont