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Inductive Learning with BCT

Philip K. Chan

Title:
Inductive Learning with BCT
Author(s):
Chan, Philip K.
Date:
Type:
Technical reports
Department:
Computer Science
Persistent URL:
Series:
Columbia University Computer Science Technical Reports
Part Number:
CUCS-451-89
Publisher:
Department of Computer Science, Columbia University
Publisher Location:
New York
Abstract:
BCT (Binary Classification Tree) is a system that learns from examples and represents learned concepts as a binary polythetic decision tree. Polythetic trees differ from monothetic decision trees in that a logical combination of multiple (versus a single) attribute values may label each tree arc. Statistical evaluations are used to recursively partition the concept space in two and expand the tree. As with standard decision trees, leaves denote classifications. Classes are predicted for unseen instances by traversing appropriate branches in the tree to the leaves. Empirical results demonstrated that BCT is generally more accurate or comparable to two earlier systems.
Subject(s):
Computer science
Item views
144
Metadata:
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Suggested Citation:
Philip K. Chan, 1989, Inductive Learning with BCT, Columbia University Academic Commons, http://hdl.handle.net/10022/AC:P:29564.

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