Inductive Learning with BCT
- Inductive Learning with BCT
- Chan, Philip K.
- Technical reports
- Computer Science
- Persistent URL:
- Columbia University Computer Science Technical Reports
- Part Number:
- Department of Computer Science, Columbia University
- Publisher Location:
- New York
- 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.
- Computer science
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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.