A Further Comparison of Splitting Rules for Decision-Tree Induction

Machine Learning

Volumn 8, Issue 1, 1992, Pages 75-85

  Buntine, Wray ^a,b   Niblett, Tim ^c  

^a ALAN TURING INSTITUTE   (United Kingdom)

^b NASA AMES RESEARCH CENTER   (United States)

^c NONE

Author keywords

comparative studies; Decision trees; induction; noisy data

Indexed keywords

EID: 0002117591     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1023/A:1022686419106     Document Type: Article

References (15)

1. Learning classification rules using Bayes
2. Assistant86: A knowledge-elicitation tool for sophisticated users
3. The CN2 induction algorithm
4. An empirical comparison of ID3 and back-propagation and machine learning classification methods
5. Multiple measurements in taxonomic problems
6. Machine learning as an experimental science
7. An empirical comparison of selection measures for decision-tree induction
8. An experimental comparison of symbolic and connectionist learning algorithms
9. Induction of decision trees
10. Simplifying decision trees
11. Unknown attribute values in induction
12. Inductive knowledge acquisition: A case study
13. Incremental learning from noisy data
14. An empirical comparison of pattern recognition, neural nets, and machine learning classification methods