Abstract
The formula for scaling how much information in relations on the finite universe is proposed, which is called the entropy of relation R and denoted by H (R). Based on the concept of H (R), the entropy of predicates and the information of propositions are measured. We can use these measures to evaluate predicates and choose the most appropriate predicate for some given cartesian set. At last, H (R) is used to induce decision tree. The experiment show that the new induction algorithm denoted by IDIR do better than ID3 on the aspects of nodes and test time.
Supported by The Nature Science Foundation of China (Grant No.60474023), Research Fund for Doctoral Program of Higher Education (20020027013), Science Technology Key Project Fund of Ministry of Education (03184) and Major State Basic Research Development Program of China (2002CB312200).
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© 2005 Springer-Verlag Berlin Heidelberg
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Hu, D., Li, H. (2005). The Entropy of Relations and a New Approach for Decision Tree Learning. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_47
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DOI: https://doi.org/10.1007/11540007_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
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