The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A further comparison of splitting rules for decision-tree induction
Open accessAbstract
One approach to learning classification rules from examples is to build decision trees. A review and comparison paper by Mingers (Mingers, 1989) looked at the first stage of tree building, which uses a “splitting rule” to grow trees with a greedy recursive partitioning algorithm. That paper considered a number of different measures and experimentally examined their behavior on four domains. The main conclusion was that a random splitting rule does not significantly decrease classificational accuracy. This note suggests an alternative experimental method and presents additional results on further domains. Our results indicate that random splitting leads to increased error. These results are at variance with those presented by Mingers.
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Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Machine learning as an experimental science
Langley, P. (1988) · Machine Learning · doi:10.1007/bf00115008 · Cited by 187
Cited by
Data mining: concepts and techniques
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Geurts, P., Ernst, D., & Wehenkel, L. (2006) · Machine Learning · doi:10.1007/s10994-006-6226-1 · Cited by 9,300
Decision tree classification of land cover from remotely sensed data
Friedl, M. A., & Brodley, C. E. (1997) · Remote Sensing of Environment · doi:10.1016/s0034-4257(97)00049-7 · Cited by 1,542
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Murthy, S. K. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009744630224 · Cited by 991
Series in Machine Perception and Artificial Intelligence
(2012) · Series in machine perception and artificial intelligence · doi:10.1142/smpai · Cited by 365
Metadata
- Authors
- Buntine, W., & Niblett, T.
- Venue
- Machine Learning
- Published
- 1992-01
- Volume
- 8
- Issue
- 1
- Pages
- 75-85
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1969223365
- Cited by
- 269
- References
- 3
- FWCI
- 40.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
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