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Active learning of causal bayesian networks using ontologies: a case study

Abstract : Within the last years, probabilistic causality has become a very active research topic in artificial intelligence and statistics communities. Due to its high impact in various applications involving reasoning tasks, machine learning researchers have proposed a number of techniques to learn Causal Bayesian Networks. Within the existing works in this direction, few studies have explicitly considered the role that decisional guidance might play to alternate between observational and experimental data processing. In this paper, we spread our previous works which foster greater collaboration between causal discovery and ontology evolution so as to evaluate them on real case study.
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Contributor : Philippe Leray Connect in order to contact the contributor
Submitted on : Friday, April 17, 2020 - 4:00:09 PM
Last modification on : Wednesday, April 27, 2022 - 4:17:09 AM


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  • HAL Id : hal-00864156, version 1


Montassar Ben Messaoud, Philippe Leray, Nahla Ben Amor. Active learning of causal bayesian networks using ontologies: a case study. International Joint Conference on Neural Networks, 2013, Dallas, United States. pp.1-6. ⟨hal-00864156⟩



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