Improving stability of decision trees

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 16, Issue 2, 2002, Pages 145-159

  Last, Mark ^a   Maimon, Oded ^b   Minkov, Einat ^b  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

^b TEL AVIV UNIVERSITY   (Israel)

Author keywords

Classification accuracy; Decision trees; Info fuzzy network; Multiple models; Output complexity; Similarity; Stability

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATA MINING; DECISION THEORY; FUZZY SETS; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; SEMANTICS; SYNTACTICS; SYSTEM STABILITY; TREES (MATHEMATICS);

DECISION TREE ALGORITHMS; DEGREE OF CERTAINTY; INFO-FUZZY-NETWORK; LIKELIHOOD RATIO TEST; META LEARNER; SEMANTIC STABILITY; SYNTACTIC COMPLEXITY; TRAINING SET;

FUZZY CONTROL;

EID: 0036505420     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001402001599     Document Type: Article

References (25)

1. Learning structural descriptions of patterns: A new technique for conditional clustering and rule generation
2. UCI repository of machine learning databases
3. Bagging predictors
4. A machine learning method for generation of a neural network architecture: A continuous ID3 algorithm
5. Knowledge acquisition from examples via multiple models
6. Why does bagging work? A Bayesian account and its implications
7. Knowledge discovery via multiple models
8. The cascade-correlation learning architecture
9. Multi-interval discretization of continuous-valued attributes for classification learning
10. From data mining to knowledge discovery: An overview
11. Experiments with a new boosting algorithm
12. Tree matching problems with applications to structured text databases
13. Oblivious decision trees, graphs, and top-down pruning
14. Knowledge discovery and data mining
15. Matching hierarchical structures using association graphs
16. Connectivity and performance tradeoffs in the cascade correlation learning architecture
17. Induction of decision trees
18. Bias and the quantification of stability