Computational Intelligence approaches to causality detection.

Computational Intelligence approaches to causality detection.

Abstract

Discovering interdependencies and causal relationships is one of the most relevant challenges raised by the information era. As more and better data become available, there is an urgent need for data-driven techniques with the capability of efficiently detecting hidden interactions. As such, this important issue is receiving increasing attention in the recent literature. The aim of the Learning Causality Special Session is to bring together theory-oriented and practitioners of this fascinating discipline. The main streams of causality detection by Computational Intelligence will be covered, namely, the probabilistic, information-theoretic, and Granger approaches.

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Authors
  • Schindlerova, Katerina
  • Verdes, Pablo F.
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Full Paper in Proceedings)
Event Title
European Symposium on Artificial Neural Networks (ESANN)
Divisions
Data Mining
Subjects
Kuenstliche Intelligenz
Event Type
Conference
Series Name
Proceedings of ESANN 2007
ISSN/ISBN
2-930307-07-2
Page Range
pp. 433-440
Date
April 2007
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