Knowledge discovery from epidemiological databases
ARC II is a learning system that allows to discover relationships from symbolic data. The learning strategy is based on probabilistic induction and produces dependence relationships between a fact and a set of facts. The system also takes into account dated facts or events in order to produce causal relationships between an event (effect), and a set of facts (cause) including at least one event. Relationships are represented under the form of uncertain production rules. The algorithm ensures that (1) the rules are complete, i.e. that the premises include all known relevant facts and (2) the rules are elementary, i.e. no irrelevant fact belongs to the premises. ARC II has been applied to the analysis of medical data.
KeywordsKnowledge Discovery Induction Dependence and Causal Relationships
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- [Abrams91]D.I. Abrams, Acquired Immunodeficiency Syndrome and Related Malignancies: a Topical Overview, Seminar in Oncology, Vol 18, n∘ 5, pp 41–45, 1991Google Scholar
- [Clark89]P. Clark, T. Niblett, The CN2 Induction Algorithm. Machine Learning 3: 261–283, 1989Google Scholar
- [Dover91]J.S. Dover, Cutaneous Manifestations of Human Immunodeficiency Virus Infection. Part I, Archives of Dermatology, Vol 127, pp 1383–1391, 1991Google Scholar
- [Michalski90]R. S. Michalski, Y. Kodratoff, Research in Machine Learning: Recent Progress, Classification of Methods and Future Directions. Machine Learning: an Artificial Intelligence Approach, Vol III, pp 3–30, 1990Google Scholar
- [Pavillon95]G. Pavillon, ARC II: Un Algorithme d'Apprentissage par Induction Probabiliste, Thèse de Doctorat, Paris VI, 1995Google Scholar
- [Piatestky91]G. Piatestky-Shapiro, Discovery, Analysis, and Presentation of Strong Rules. Knowledge Discovery in Databases, AAAI Press/The MIT Press, 229–248, 1991Google Scholar
- [Quinlan86]J.R. Quinlan, Induction of Decision Trees. Machine Learning 1, pp 81–106, 1986Google Scholar
- [Smyth91]P. Smyth, R.M. Goodman, Rule Induction Using Information Theory. Knowledge Discovery in Databases, AAAI Press/The MIT Press, 159–176, 1991Google Scholar
- [Suppes70]P. Suppes, “A Probabilistic Theory of Causality”, Acta Philosophica Fennica, Fasc XXIV, 1970Google Scholar