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French

An asymmetric entropy measure for decision trees

Presented at Paris, France, 2006
Publication date2006
Abstract

In this paper we present a new entropy measure to grow decision trees. This measure has the characteristic to be asymmetric, allowing the user to grow trees which better correspond to his expectation in terms of recall and precision on each class. Then we propose decision rules adapted to such trees. Experiments have been realized on real medical data from breast cancer screening units.

Keywords
  • Decision trees
  • Entropy
  • Class imbalance
  • Asymmetric learning
  • Decision rules
Citation (ISO format)
MARCELLIN, Simon, ZIGHED, Djamel A., RITSCHARD, Gilbert. An asymmetric entropy measure for decision trees. In: 11th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2006. Paris, France. [s.l.] : [s.n.], 2006. p. 1292–1299.
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  • PID : unige:4531
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