Adding domain knowledge to SBL through feature construction

Authors:
Christopher John Matheus
Affiliations:
GTE Laboratories Incorporated, Waltham, MA
Venue:
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Year:
1990

Citing 7
Cited 1

Simplifying decision trees

International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Feature construction: an analytic framework and an application to decision trees

Feature construction: an analytic framework and an application to decision trees
Proceedings of the sixth international workshop on Machine learning

Proceedings of the sixth international workshop on Machine learning
Learning DNF by decision trees

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Constructive induction on decision trees

IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Duce, an oracle-based approach to constructive induction

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Substantial constructive induction using layered information compression: tractable feature formation in search

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Unsupervised feature construction for improving data representation and semantics

Journal of Intelligent Information Systems

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Abstract

This paper presents two methods for adding domain knowledge to similarity-based learning through feature construction, a form of representation change in which new features are constructed from relationships detected among existing features. In the first method, domain-knowledge constraints are used to eliminate less desirable new features before they are constructed. In the second method, domain-dependent transformations generalize new features in ways meaningful to the current problem. These two uses of domain knowledge are illustrated in CITRE where they are shown to improve hypothesis accuracy and conciseness on a tic-tat-toe classification problem.