Automated extraction of attribute hierarchies for an improved decision-tree classifier

Engineering Applications of Artificial Intelligence

Volumn 8, Issue 4, 1995, Pages 391-399

  Nakasuka, Shinichi ^a   Koishi, Takazo ^b  

^a UNIVERSITY OF TOKYO   (Japan)

^b IBM JAPAN LTD   (Japan)

Author keywords

conceptual distance; Decision tree classifiers; fault diagnosis; generalization; knowledge acquisition; knowledge bases

Indexed keywords

AUTOMATION; CLASSIFICATION (OF INFORMATION); DECISION THEORY; HIERARCHICAL SYSTEMS; ITERATIVE METHODS; KNOWLEDGE ACQUISITION; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; PRINTED CIRCUIT BOARDS; PROBABILITY; PROBLEM SOLVING;

CONCEPTUAL DISTANCE; DECISION RULES; DECISION TREE CLASSIFIERS; DECISION TREES; FAULT DIAGNOSIS;

FEATURE EXTRACTION;

EID: 0029354284     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/0952-1976(95)00022-S     Document Type: Article

References (15)

1. Automated classification of nucleted blood cells using a binary tree classifier
2. Bayesian and decision tree approaches for pattern recognition including feature measurement costs
3. Hierarchical classifier design using mutual information
4. Application of a multilayer decision tree in computer recognition of Chinese characters
5. Large tree classifier with heuristic search and global training
6. An automated approach to the design of decision tree classifiers
7. Learning efficient classification procedures and their application to chess endgames
8. The effect of noise on concept learning
9. Simplifying decision trees
10. Intuction of decision trees
11. Probabilistic decision trees
12. Learning based on conceptual distance
13. Learning by experimentation: acquiring and refining problem-solving heuristics
14. OGUST: a system that learns using domain properties expressed as theorems
15. Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool