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Entropy measures of flow graphs with applications to decision trees
Author(s)
Pattaraintakorn, Puntip
Date Issued
August 27, 2009
Type
Conference Paper
Abstract
Entropy is a fundamental principle in many disciplines such as information theory, thermodynamics, and more recently, artificial intelligence. In this article, a measure of entropy on Pawlak's mathematical flow graph is introduced. The predictability and quality of a flow graph can be derived directly from the entropy. An application to decision tree generation from a flow graph is examined. In particular, entropy measures on flow graphs lead to a new methodology of reasoning from data and shows rigorous relationships between flow graphs, entropy and decision trees. © 2009 Springer Berlin Heidelberg.
Citation
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 5589 LNAI, 618-625, 2009
Subjects